import numpy as np
import pandas as pd

import random
random.seed(28)
np.random.seed(28)

import matplotlib.pyplot as plt
from sklearn.metrics import (confusion_matrix, precision_recall_curve, auc,
                             roc_curve, recall_score, classification_report, f1_score,
                             precision_recall_fscore_support)
import os
import copy
from sklearn.metrics import mean_absolute_error
pd.options.display.precision = 15
from collections import defaultdict
import lightgbm as lgb
import xgboost as xgb
import time
from collections import Counter
import datetime
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import StratifiedKFold, KFold, RepeatedKFold, GroupKFold, GridSearchCV, train_test_split, TimeSeriesSplit, RepeatedStratifiedKFold
from sklearn import metrics
import gc
import seaborn as sns
import warnings
warnings.filterwarnings("ignore")
from bayes_opt import BayesianOptimization
#import eli5
import shap
from IPython.display import HTML
import json

import matplotlib.pyplot as plt
%matplotlib inline
import os
import time
import datetime
import gc
import matplotlib.pyplot as plt
import seaborn as sns

from sklearn import metrics
pd.set_option('max_rows', 500)
import re

import os

pd.set_option('display.max_columns', 1000)
pd.set_option('display.max_rows', 500)
pd.set_option('display.width', 1000)
pd.set_option('display.float_format', '{:20,.2f}'.format)
pd.set_option('display.max_colwidth', -1)

np.random.seed(2206)

Read the data

train = pd.read_csv("../data/training_v2.csv")
samplesubmission = pd.read_csv("../data/samplesubmission.csv")
test = pd.read_csv("../data/unlabeled.csv")
dictionary = pd.read_csv("../data/WiDS Datathon 2020 Dictionary.csv")
solution_template = pd.read_csv("../data/solution_template.csv")

print('train ' , train.shape)
print('test ' , test.shape)
print('samplesubmission ' , samplesubmission.shape)
print('solution_template ' , solution_template.shape)
print('dictionary ' , dictionary.shape)

train  (91713, 186)
test  (39308, 186)
samplesubmission  (3, 2)
solution_template  (39308, 2)
dictionary  (188, 6)

dico = pd.DataFrame(dictionary.T.head(6))
dico.columns=list(dico.loc[dico.index == 'Variable Name'].unstack())
dico = dico.loc[dico.index != 'Variable Name']
dico.columns
train_stat = pd.DataFrame(train.describe())
train_stat2 = pd.concat([dico,train_stat],axis=0)
train_stat2.head(20)

	age	aids	albumin_apache	apache_2_bodysystem	apache_2_diagnosis	apache_3j_bodysystem	apache_3j_diagnosis	apache_4a_hospital_death_prob	apache_4a_icu_death_prob	apache_post_operative	arf_apache	bilirubin_apache	bmi	bun_apache	cirrhosis	creatinine_apache	d1_albumin_max	d1_albumin_min	d1_arterial_pco2_max	d1_arterial_pco2_min	d1_arterial_ph_max	d1_arterial_ph_min	d1_arterial_po2_max	d1_arterial_po2_min	d1_bilirubin_max	d1_bilirubin_min	d1_bun_max	d1_bun_min	d1_calcium_max	d1_calcium_min	d1_creatinine_max	d1_creatinine_min	d1_diasbp_invasive_max	d1_diasbp_invasive_min	d1_diasbp_max	d1_diasbp_min	d1_diasbp_noninvasive_max	d1_diasbp_noninvasive_min	d1_glucose_max	d1_glucose_min	d1_hco3_max	d1_hco3_min	d1_heartrate_max	d1_heartrate_min	d1_hemaglobin_max	d1_hemaglobin_min	d1_hematocrit_max	d1_hematocrit_min	d1_inr_max	d1_inr_min	d1_lactate_max	d1_lactate_min	d1_mbp_invasive_max	d1_mbp_invasive_min	d1_mbp_max	d1_mbp_min	d1_mbp_noninvasive_max	d1_mbp_noninvasive_min	d1_pao2fio2ratio_max	d1_pao2fio2ratio_min	d1_platelets_max	d1_platelets_min	d1_potassium_max	d1_potassium_min	d1_resprate_max	d1_resprate_min	d1_sodium_max	d1_sodium_min	d1_spo2_max	d1_spo2_min	d1_sysbp_invasive_max	d1_sysbp_invasive_min	d1_sysbp_max	d1_sysbp_min	d1_sysbp_noninvasive_max	d1_sysbp_noninvasive_min	d1_temp_max	d1_temp_min	d1_wbc_max	d1_wbc_min	diabetes_mellitus	elective_surgery	encounter_id	ethnicity	fio2_apache	gcs_eyes_apache	gcs_motor_apache	gcs_unable_apache	gcs_verbal_apache	gender	glucose_apache	h1_albumin_max	h1_albumin_min	h1_arterial_pco2_max	h1_arterial_pco2_min	h1_arterial_ph_max	h1_arterial_ph_min	h1_arterial_po2_max	h1_arterial_po2_min	h1_bilirubin_max	h1_bilirubin_min	h1_bun_max	h1_bun_min	h1_calcium_max	h1_calcium_min	h1_creatinine_max	h1_creatinine_min	h1_diasbp_invasive_max	h1_diasbp_invasive_min	h1_diasbp_max	h1_diasbp_min	h1_diasbp_noninvasive_max	h1_diasbp_noninvasive_min	h1_glucose_max	h1_glucose_min	h1_hco3_max	h1_hco3_min	h1_heartrate_max	h1_heartrate_min	h1_hemaglobin_max	h1_hemaglobin_min	h1_hematocrit_max	h1_hematocrit_min	h1_inr_max	h1_inr_min	h1_lactate_max	h1_lactate_min	h1_mbp_invasive_max	h1_mbp_invasive_min	h1_mbp_max	h1_mbp_min	h1_mbp_noninvasive_max	h1_mbp_noninvasive_min	h1_pao2fio2ratio_max	h1_pao2fio2ratio_min	h1_platelets_max	h1_platelets_min	h1_potassium_max	h1_potassium_min	h1_resprate_max	h1_resprate_min	h1_sodium_max	h1_sodium_min	h1_spo2_max	h1_spo2_min	h1_sysbp_invasive_max	h1_sysbp_invasive_min	h1_sysbp_max	h1_sysbp_min	h1_sysbp_noninvasive_max	h1_sysbp_noninvasive_min	h1_temp_max	h1_temp_min	h1_wbc_max	h1_wbc_min	heart_rate_apache	height	hematocrit_apache	hepatic_failure	hospital_admit_source	hospital_death	hospital_id	icu_admit_source	icu_admit_type	icu_id	icu_stay_type	icu_type	immunosuppression	intubated_apache	leukemia	lymphoma	map_apache	paco2_apache	paco2_for_ph_apache	pao2_apache	patient_id	ph_apache	pre_icu_los_days	pred	readmission_status	resprate_apache	sodium_apache	solid_tumor_with_metastasis	temp_apache	urineoutput_apache	ventilated_apache	wbc_apache	weight
Category	demographic	APACHE comorbidity	APACHE covariate	APACHE grouping	APACHE covariate	APACHE grouping	APACHE covariate	APACHE prediction	APACHE prediction	APACHE covariate	APACHE covariate	APACHE covariate	demographic	APACHE covariate	APACHE comorbidity	APACHE covariate	labs	labs	labs blood gas	labs blood gas	labs blood gas	labs blood gas	labs blood gas	labs blood gas	labs	labs	labs	labs	labs	labs	labs	labs	vitals	vitals	vitals	vitals	vitals	vitals	labs	labs	labs	labs	vitals	vitals	labs	labs	labs	labs	labs	labs	labs	labs	vitals	vitals	vitals	vitals	vitals	vitals	labs blood gas	labs blood gas	labs	labs	labs	labs	vitals	vitals	labs	labs	vitals	vitals	vitals	vitals	vitals	vitals	vitals	vitals	vitals	vitals	labs	labs	APACHE comorbidity	demographic	identifier	demographic	APACHE covariate	APACHE covariate	APACHE covariate	APACHE covariate	APACHE covariate	demographic	APACHE covariate	labs	labs	labs blood gas	labs blood gas	labs blood gas	labs blood gas	labs blood gas	labs blood gas	labs	labs	labs	labs	labs	labs	labs	labs	vitals	vitals	vitals	vitals	vitals	vitals	labs	labs	labs	labs	vitals	vitals	labs	labs	labs	labs	labs	labs	labs	labs	vitals	vitals	vitals	vitals	vitals	vitals	labs blood gas	labs blood gas	labs	labs	labs	labs	vitals	vitals	labs	labs	vitals	vitals	vitals	vitals	vitals	vitals	vitals	vitals	vitals	vitals	labs	labs	APACHE covariate	demographic	APACHE covariate	APACHE comorbidity	demographic	demographic	identifier	demographic	demographic	demographic	demographic	demographic	APACHE comorbidity	APACHE covariate	APACHE comorbidity	APACHE comorbidity	APACHE covariate	APACHE covariate	APACHE covariate	APACHE covariate	identifier	APACHE covariate	demographic	GOSSIS example prediction	demographic	APACHE covariate	APACHE covariate	APACHE comorbidity	APACHE covariate	APACHE covariate	APACHE covariate	APACHE covariate	demographic
Unit of Measure	Years	None	g/L	None	None	None	None	None	None	None	None	micromol/L	kilograms/metres^2	mmol/L	None	micromol/L	None	g/L	Millimetres of mercury	Millimetres of mercury	None	None	Millimetres of mercury	Millimetres of mercury	micromol/L	micromol/L	mmol/L	mmol/L	mmol/L	mmol/L	micromol/L	micromol/L	Millimetres of mercury	Millimetres of mercury	Millimetres of mercury	Millimetres of mercury	Millimetres of mercury	Millimetres of mercury	mmol/L	mmol/L	mmol/L	None	Beats per minute	Beats per minute	g/dL	g/dL	Fraction	Fraction	micromol/L	micromol/L	mmol/L	mmol/L	Millimetres of mercury	Millimetres of mercury	Millimetres of mercury	Millimetres of mercury	Millimetres of mercury	Millimetres of mercury	Fraction	Fraction	10^9/L	10^9/L	mmol/L	mmol/L	Breaths per minute	Breaths per minute	mmol/L	mmol/L	Percentage	Percentage	Millimetres of mercury	Millimetres of mercury	Millimetres of mercury	Millimetres of mercury	Millimetres of mercury	Millimetres of mercury	Degrees Celsius	Degrees Celsius	10^9/L	10^9/L	None	None	None	None	Fraction	None	None	None	None	None	mmol/L	None	g/L	Millimetres of mercury	Millimetres of mercury	None	None	Millimetres of mercury	Millimetres of mercury	micromol/L	micromol/L	mmol/L	mmol/L	mmol/L	mmol/L	micromol/L	micromol/L	Millimetres of mercury	Millimetres of mercury	Millimetres of mercury	Millimetres of mercury	Millimetres of mercury	Millimetres of mercury	mmol/L	mmol/L	mmol/L	None	Beats per minute	Beats per minute	g/dL	g/dL	Fraction	Fraction	micromol/L	micromol/L	mmol/L	mmol/L	Millimetres of mercury	Millimetres of mercury	Millimetres of mercury	Millimetres of mercury	Millimetres of mercury	Millimetres of mercury	Fraction	Fraction	10^9/L	10^9/L	mmol/L	mmol/L	Breaths per minute	Breaths per minute	mmol/L	mmol/L	Percentage	Percentage	Millimetres of mercury	Millimetres of mercury	Millimetres of mercury	Millimetres of mercury	Millimetres of mercury	Millimetres of mercury	Degrees Celsius	Degrees Celsius	10^9/L	10^9/L	Beats per minute	centimetres	Fraction	None	None	None	None	None	None	None	None	None	None	None	None	None	Millimetres of mercury	Millimetres of mercury	Millimetres of mercury	Millimetres of mercury	None	None	Days	None	None	Breaths per minute	mmol/L	None	Degrees Celsius	Millilitres	None	10^9/L	kilograms
Data Type	numeric	binary	numeric	string	string	string	string	numeric	numeric	binary	binary	numeric	string	numeric	binary	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	binary	binary	integer	string	numeric	integer	integer	binary	integer	string	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	numeric	binary	string	binary	integer	string	string	integer	string	string	binary	binary	binary	binary	numeric	numeric	numeric	numeric	integer	numeric	numeric	numeric	binary	numeric	numeric	binary	numeric	numeric	binary	numeric	numeric
Description	The age of the patient on unit admission	Whether the patient has a definitive diagnosis of acquired immune deficiency syndrome (AIDS) (not HIV positive alone)	The albumin concentration measured during the first 24 hours which results in the highest APACHE III score	Admission diagnosis group for APACHE II	The APACHE II diagnosis for the ICU admission	Admission diagnosis group for APACHE III	The APACHE III-J sub-diagnosis code which best describes the reason for the ICU admission	The APACHE IVa probabilistic prediction of in-hospital mortality for the patient which utilizes the APACHE III score and other covariates, including diagnosis.	The APACHE IVa probabilistic prediction of in ICU mortality for the patient which utilizes the APACHE III score and other covariates, including diagnosis	The APACHE operative status; 1 for post-operative, 0 for non-operative	Whether the patient had acute renal failure during the first 24 hours of their unit stay, defined as a 24 hour urine output <410ml, creatinine >=133 micromol/L and no chronic dialysis	The bilirubin concentration measured during the first 24 hours which results in the highest APACHE III score	The body mass index of the person on unit admission	The blood urea nitrogen concentration measured during the first 24 hours which results in the highest APACHE III score	Whether the patient has a history of heavy alcohol use with portal hypertension and varices, other causes of cirrhosis with evidence of portal hypertension and varices, or biopsy proven cirrhosis. This comorbidity does not apply to patients with a functioning liver transplant.	The creatinine concentration measured during the first 24 hours which results in the highest APACHE III score	The lowest albumin concentration of the patient in their serum during the first 24 hours of their unit stay	The lowest albumin concentration of the patient in their serum during the first 24 hours of their unit stay	The highest arterial partial pressure of carbon dioxide for the patient during the first 24 hours of their unit stay	The lowest arterial partial pressure of carbon dioxide for the patient during the first 24 hours of their unit stay	The highest arterial pH for the patient during the first 24 hours of their unit stay	The lowest arterial pH for the patient during the first 24 hours of their unit stay	The highest arterial partial pressure of oxygen for the patient during the first 24 hours of their unit stay	The lowest arterial partial pressure of oxygen for the patient during the first 24 hours of their unit stay	The highest bilirubin concentration of the patient in their serum or plasma during the first 24 hours of their unit stay	The lowest bilirubin concentration of the patient in their serum or plasma during the first 24 hours of their unit stay	The highest blood urea nitrogen concentration of the patient in their serum or plasma during the first 24 hours of their unit stay	The lowest blood urea nitrogen concentration of the patient in their serum or plasma during the first 24 hours of their unit stay	The highest calcium concentration of the patient in their serum during the first 24 hours of their unit stay	The lowest calcium concentration of the patient in their serum during the first 24 hours of their unit stay	The highest creatinine concentration of the patient in their serum or plasma during the first 24 hours of their unit stay	The lowest creatinine concentration of the patient in their serum or plasma during the first 24 hours of their unit stay	The patient's highest diastolic blood pressure during the first 24 hours of their unit stay, invasively measured	The patient's lowest diastolic blood pressure during the first 24 hours of their unit stay, invasively measured	The patient's highest diastolic blood pressure during the first 24 hours of their unit stay, either non-invasively or invasively measured	The patient's lowest diastolic blood pressure during the first 24 hours of their unit stay, either non-invasively or invasively measured	The patient's highest diastolic blood pressure during the first 24 hours of their unit stay, non-invasively measured	The patient's lowest diastolic blood pressure during the first 24 hours of their unit stay, non-invasively measured	The highest glucose concentration of the patient in their serum or plasma during the first 24 hours of their unit stay	The lowest glucose concentration of the patient in their serum or plasma during the first 24 hours of their unit stay	The highest bicarbonate concentration for the patient in their serum or plasma during the first 24 hours of their unit stay	The lowest bicarbonate concentration for the patient in their serum or plasma during the first 24 hours of their unit stay	The patient's highest heart rate during the first 24 hours of their unit stay	The patient's lowest heart rate during the first 24 hours of their unit stay	The highest hemoglobin concentration for the patient during the first 24 hours of their unit stay	The lowest hemoglobin concentration for the patient during the first 24 hours of their unit stay	The highest volume proportion of red blood cells in a patient's blood during the first 24 hours of their unit stay, expressed as a fraction	The lowest volume proportion of red blood cells in a patient's blood during the first 24 hours of their unit stay, expressed as a fraction	The highest international normalized ratio for the patient during the first 24 hours of their unit stay	The lowest international normalized ratio for the patient during the first 24 hours of their unit stay	The highest lactate concentration for the patient in their serum or plasma during the first 24 hours of their unit stay	The lowest lactate concentration for the patient in their serum or plasma during the first 24 hours of their unit stay	The patient's highest mean blood pressure during the first 24 hours of their unit stay, invasively measured	The patient's lowest mean blood pressure during the first 24 hours of their unit stay, invasively measured	The patient's highest mean blood pressure during the first 24 hours of their unit stay, either non-invasively or invasively measured	The patient's lowest mean blood pressure during the first 24 hours of their unit stay, either non-invasively or invasively measured	The patient's highest mean blood pressure during the first 24 hours of their unit stay, non-invasively measured	The patient's lowest mean blood pressure during the first 24 hours of their unit stay, non-invasively measured	The highest fraction of inspired oxygen for the patient during the first 24 hours of their unit stay	The lowest fraction of inspired oxygen for the patient during the first 24 hours of their unit stay	The highest platelet count for the patient during the first 24 hours of their unit stay	The lowest platelet count for the patient during the first 24 hours of their unit stay	The highest potassium concentration for the patient in their serum or plasma during the first 24 hours of their unit stay	The lowest potassium concentration for the patient in their serum or plasma during the first 24 hours of their unit stay	The patient's highest respiratory rate during the first 24 hours of their unit stay	The patient's lowest respiratory rate during the first 24 hours of their unit stay	The highest sodium concentration for the patient in their serum or plasma during the first 24 hours of their unit stay	The lowest sodium concentration for the patient in their serum or plasma during the first 24 hours of their unit stay	The patient's highest peripheral oxygen saturation during the first 24 hours of their unit stay	The patient's lowest peripheral oxygen saturation during the first 24 hours of their unit stay	The patient's highest systolic blood pressure during the first 24 hours of their unit stay, invasively measured	The patient's lowest systolic blood pressure during the first 24 hours of their unit stay, invasively measured	The patient's highest systolic blood pressure during the first 24 hours of their unit stay, either non-invasively or invasively measured	The patient's lowest systolic blood pressure during the first 24 hours of their unit stay, either non-invasively or invasively measured	The patient's highest systolic blood pressure during the first 24 hours of their unit stay, non-invasively measured	The patient's lowest systolic blood pressure during the first 24 hours of their unit stay, non-invasively measured	The patient's highest core temperature during the first 24 hours of their unit stay, invasively measured	The patient's lowest core temperature during the first 24 hours of their unit stay	The highest white blood cell count for the patient during the first 24 hours of their unit stay	The lowest white blood cell count for the patient during the first 24 hours of their unit stay	Whether the patient has been diagnosed with diabetes, either juvenile or adult onset, which requires medication.	Whether the patient was admitted to the hospital for an elective surgical operation	Unique identifier associated with a patient unit stay	The common national or cultural tradition which the person belongs to	The fraction of inspired oxygen from the arterial blood gas taken during the first 24 hours of unit admission which produces the highest APACHE III score for oxygenation	The eye opening component of the Glasgow Coma Scale measured during the first 24 hours which results in the highest APACHE III score	The motor component of the Glasgow Coma Scale measured during the first 24 hours which results in the highest APACHE III score	Whether the Glasgow Coma Scale was unable to be assessed due to patient sedation	The verbal component of the Glasgow Coma Scale measured during the first 24 hours which results in the highest APACHE III score	The genotypical sex of the patient	The glucose concentration measured during the first 24 hours which results in the highest APACHE III score	The lowest albumin concentration of the patient in their serum during the first hour of their unit stay	The lowest albumin concentration of the patient in their serum during the first hour of their unit stay	The highest arterial partial pressure of carbon dioxide for the patient during the first hour of their unit stay	The lowest arterial partial pressure of carbon dioxide for the patient during the first hour of their unit stay	The highest arterial pH for the patient during the first hour of their unit stay	The lowest arterial pH for the patient during the first hour of their unit stay	The highest arterial partial pressure of oxygen for the patient during the first hour of their unit stay	The lowest arterial partial pressure of oxygen for the patient during the first hour of their unit stay	The highest bilirubin concentration of the patient in their serum or plasma during the first hour of their unit stay	The lowest bilirubin concentration of the patient in their serum or plasma during the first hour of their unit stay	The highest blood urea nitrogen concentration of the patient in their serum or plasma during the first hour of their unit stay	The lowest blood urea nitrogen concentration of the patient in their serum or plasma during the first hour of their unit stay	The highest calcium concentration of the patient in their serum during the first hour of their unit stay	The lowest calcium concentration of the patient in their serum during the first hour of their unit stay	The highest creatinine concentration of the patient in their serum or plasma during the first hour of their unit stay	The lowest creatinine concentration of the patient in their serum or plasma during the first hour of their unit stay	The patient's highest diastolic blood pressure during the first hour of their unit stay, invasively measured	The patient's lowest diastolic blood pressure during the first hour of their unit stay, invasively measured	The patient's highest diastolic blood pressure during the first hour of their unit stay, either non-invasively or invasively measured	The patient's lowest diastolic blood pressure during the first hour of their unit stay, either non-invasively or invasively measured	The patient's highest diastolic blood pressure during the first hour of their unit stay, non-invasively measured	The patient's lowest diastolic blood pressure during the first hour of their unit stay, non-invasively measured	The highest glucose concentration of the patient in their serum or plasma during the first hour of their unit stay	The lowest glucose concentration of the patient in their serum or plasma during the first hour of their unit stay	The highest bicarbonate concentration for the patient in their serum or plasma during the first hour of their unit stay	The lowest bicarbonate concentration for the patient in their serum or plasma during the first hour of their unit stay	The patient's highest heart rate during the first hour of their unit stay	The patient's lowest heart rate during the first hour of their unit stay	The highest hemoglobin concentration for the patient during the first hour of their unit stay	The lowest hemoglobin concentration for the patient during the first hour of their unit stay	The highest volume proportion of red blood cells in a patient's blood during the first hour of their unit stay, expressed as a fraction	The lowest volume proportion of red blood cells in a patient's blood during the first hour of their unit stay, expressed as a fraction	The highest international normalized ratio for the patient during the first hour of their unit stay	The lowest international normalized ratio for the patient during the first hour of their unit stay	The highest lactate concentration for the patient in their serum or plasma during the first hour of their unit stay	The lowest lactate concentration for the patient in their serum or plasma during the first hour of their unit stay	The patient's highest mean blood pressure during the first hour of their unit stay, invasively measured	The patient's lowest mean blood pressure during the first hour of their unit stay, invasively measured	The patient's highest mean blood pressure during the first hour of their unit stay, either non-invasively or invasively measured	The patient's lowest mean blood pressure during the first hour of their unit stay, either non-invasively or invasively measured	The patient's highest mean blood pressure during the first hour of their unit stay, non-invasively measured	The patient's lowest mean blood pressure during the first hour of their unit stay, non-invasively measured	The highest fraction of inspired oxygen for the patient during the first hour of their unit stay	The lowest fraction of inspired oxygen for the patient during the first hour of their unit stay	The highest platelet count for the patient during the first hour of their unit stay	The lowest platelet count for the patient during the first hour of their unit stay	The highest potassium concentration for the patient in their serum or plasma during the first hour of their unit stay	The lowest potassium concentration for the patient in their serum or plasma during the first hour of their unit stay	The patient's highest respiratory rate during the first hour of their unit stay	The patient's lowest respiratory rate during the first hour of their unit stay	The highest sodium concentration for the patient in their serum or plasma during the first hour of their unit stay	The lowest sodium concentration for the patient in their serum or plasma during the first hour of their unit stay	The patient's highest peripheral oxygen saturation during the first hour of their unit stay	The patient's lowest peripheral oxygen saturation during the first hour of their unit stay	The patient's highest systolic blood pressure during the first hour of their unit stay, invasively measured	The patient's lowest systolic blood pressure during the first hour of their unit stay, invasively measured	The patient's highest systolic blood pressure during the first hour of their unit stay, either non-invasively or invasively measured	The patient's lowest systolic blood pressure during the first hour of their unit stay, either non-invasively or invasively measured	The patient's highest systolic blood pressure during the first hour of their unit stay, non-invasively measured	The patient's lowest systolic blood pressure during the first hour of their unit stay, non-invasively measured	The patient's highest core temperature during the first hour of their unit stay, invasively measured	The patient's lowest core temperature during the first hour of their unit stay	The highest white blood cell count for the patient during the first hour of their unit stay	The lowest white blood cell count for the patient during the first hour of their unit stay	The heart rate measured during the first 24 hours which results in the highest APACHE III score	The height of the person on unit admission	The hematocrit measured during the first 24 hours which results in the highest APACHE III score	Whether the patient has cirrhosis and additional complications including jaundice and ascites, upper GI bleeding, hepatic encephalopathy, or coma.	The location of the patient prior to being admitted to the hospital	Whether the patient died during this hospitalization	Unique identifier associated with a hospital	The location of the patient prior to being admitted to the unit	The type of unit admission for the patient	A unique identifier for the unit to which the patient was admitted	NaN	A classification which indicates the type of care the unit is capable of providing	Whether the patient has their immune system suppressed within six months prior to ICU admission for any of the following reasons; radiation therapy, chemotherapy, use of non-cytotoxic immunosuppressive drugs, high dose steroids (at least 0.3 mg/kg/day of methylprednisolone or equivalent for at least 6 months).	Whether the patient was intubated at the time of the highest scoring arterial blood gas used in the oxygenation score	Whether the patient has been diagnosed with acute or chronic myelogenous leukemia, acute or chronic lymphocytic leukemia, or multiple myeloma.	Whether the patient has been diagnosed with non-Hodgkin lymphoma.	The mean arterial pressure measured during the first 24 hours which results in the highest APACHE III score	The partial pressure of carbon dioxide from the arterial blood gas taken during the first 24 hours of unit admission which produces the highest APACHE III score for oxygenation	The partial pressure of carbon dioxide from the arterial blood gas taken during the first 24 hours of unit admission which produces the highest APACHE III score for acid-base disturbance	The partial pressure of oxygen from the arterial blood gas taken during the first 24 hours of unit admission which produces the highest APACHE III score for oxygenation	Unique identifier associated with a patient	The pH from the arterial blood gas taken during the first 24 hours of unit admission which produces the highest APACHE III score for acid-base disturbance	The length of stay of the patient between hospital admission and unit admission	Example mortality prediction, shared as a 'baseline' based on one of the GOSSIS algorithm development models.	Whether the current unit stay is the second (or greater) stay at an ICU within the same hospitalization	The respiratory rate measured during the first 24 hours which results in the highest APACHE III score	The sodium concentration measured during the first 24 hours which results in the highest APACHE III score	Whether the patient has been diagnosed with any solid tumor carcinoma (including malignant melanoma) which has evidence of metastasis.	The temperature measured during the first 24 hours which results in the highest APACHE III score	The total urine output for the first 24 hours	Whether the patient was invasively ventilated at the time of the highest scoring arterial blood gas using the oxygenation scoring algorithm, including any mode of positive pressure ventilation delivered through a circuit attached to an endo-tracheal tube or tracheostomy	The white blood cell count measured during the first 24 hours which results in the highest APACHE III score	The weight (body mass) of the person on unit admission
Example	None	1	30	Respiratory	308	Cardiovascular	1405	0.31	0.24	1	0	20	21.5	None	1	70	30	30	40	40	7.4	7.4	80	80	20	20	5	5	2.5	2.5	70	70	60	60	60	60	60	60	5	5	30	30	75	75	10	10	0.4	0.4	1	1	1	1	80	80	80	80	80	80	0.21	0.21	200	200	3.8	3.8	14	14	145	145	None	100	120	120	120	120	120	120	33	33	10	10	1	0	None	Caucasian	0.21	4	6	1	5	F	5	30	30	40	40	7.4	7.4	80	80	20	20	5	5	2.5	2.5	70	70	60	60	60	60	60	60	5	5	30	30	75	75	10	10	0.4	0.4	1	1	1	1	80	80	80	80	80	80	0.21	0.21	200	200	3.8	3.8	14	14	145	145	None	100	120	120	120	120	120	120	33	33	10	10	75	180	0.4	1	Home	0	None	Operating room	Cardiothoracic	None	None	Neurological ICU	1	0	1	1	None	40	40	80	None	7.4	3.5	0.000921	0	14	145	1	33	2000	1	10	80
count	87,485.00	90,998.00	37,334.00	NaN	90,051.00	NaN	90,612.00	83,766.00	83,766.00	91,713.00	90,998.00	33,579.00	88,284.00	72,451.00	90,998.00	72,860.00	42,617.00	42,617.00	32,442.00	32,442.00	31,590.00	31,590.00	32,451.00	32,451.00	38,040.00	38,040.00	81,199.00	81,199.00	78,644.00	78,644.00	81,544.00	81,544.00	23,729.00	23,729.00	91,548.00	91,548.00	90,673.00	90,673.00	85,906.00	85,906.00	76,642.00	76,642.00	91,568.00	91,568.00	79,566.00	79,566.00	80,059.00	80,059.00	33,772.00	33,772.00	23,317.00	23,317.00	23,936.00	23,936.00	91,493.00	91,493.00	90,234.00	90,234.00	25,705.00	25,705.00	78,269.00	78,269.00	82,128.00	82,128.00	91,328.00	91,328.00	81,518.00	81,518.00	91,380.00	91,380.00	23,754.00	23,754.00	91,554.00	91,554.00	90,686.00	90,686.00	89,389.00	89,389.00	78,539.00	78,539.00	90,998.00	91,713.00	91,713.00	NaN	20,845.00	89,812.00	89,812.00	90,676.00	89,812.00	NaN	80,677.00	7,889.00	7,889.00	15,754.00	15,754.00	15,289.00	15,289.00	15,768.00	15,768.00	7,094.00	7,094.00	16,622.00	16,622.00	15,850.00	15,850.00	16,756.00	16,756.00	16,785.00	16,785.00	88,094.00	88,094.00	84,363.00	84,363.00	39,099.00	39,099.00	15,619.00	15,619.00	88,923.00	88,923.00	18,590.00	18,590.00	18,293.00	18,293.00	33,772.00	33,772.00	7,344.00	7,344.00	16,869.00	16,869.00	87,074.00	87,074.00	82,629.00	82,629.00	11,518.00	11,518.00	16,040.00	16,040.00	19,611.00	19,611.00	87,356.00	87,356.00	19,096.00	19,096.00	87,528.00	87,528.00	16,798.00	16,798.00	88,102.00	88,102.00	84,372.00	84,372.00	69,981.00	69,981.00	15,760.00	15,760.00	90,835.00	90,379.00	71,835.00	90,998.00	NaN	91,713.00	91,713.00	NaN	NaN	91,713.00	NaN	NaN	90,998.00	90,998.00	90,998.00	90,998.00	90,719.00	20,845.00	20,845.00	20,845.00	91,713.00	20,845.00	91,713.00	NaN	91,713.00	90,479.00	73,113.00	90,998.00	87,605.00	42,715.00	90,998.00	69,701.00	88,993.00
mean	62.31	0.00	2.90	NaN	185.40	NaN	558.22	0.09	0.04	0.20	0.03	1.15	29.19	25.83	0.02	1.48	2.97	2.90	45.25	38.43	7.39	7.32	165.91	103.51	1.14	1.07	25.69	23.77	8.38	8.18	1.49	1.37	78.76	46.74	88.49	50.16	88.61	50.24	174.64	114.38	24.37	23.17	103.00	70.32	11.45	10.89	34.53	32.95	1.60	1.48	2.93	2.13	114.89	62.32	104.65	64.87	104.59	64.94	285.67	223.52	207.11	196.77	4.25	3.93	28.88	12.85	139.12	137.72	99.24	90.45	154.27	93.81	148.34	96.92	148.24	96.99	37.28	36.27	12.48	11.31	0.23	0.18	65,606.08	NaN	0.60	3.47	5.47	0.01	3.99	NaN	160.33	3.03	3.03	44.67	43.38	7.34	7.33	163.84	144.15	1.10	1.10	25.84	25.82	8.28	8.28	1.53	1.53	67.97	56.14	75.35	62.84	75.81	63.27	167.99	159.22	22.50	22.42	92.23	83.66	11.19	11.04	33.67	33.22	1.60	1.48	3.07	3.02	94.88	75.97	91.61	79.40	91.59	79.71	244.40	235.93	196.10	195.48	4.20	4.15	22.63	17.21	138.24	137.90	98.04	95.17	138.70	114.83	133.25	116.36	133.05	116.55	36.71	36.61	13.46	13.42	99.71	169.64	32.99	0.01	NaN	0.09	105.67	NaN	NaN	508.36	NaN	NaN	0.03	0.15	0.01	0.00	88.02	42.18	42.18	131.15	65,537.13	7.35	0.84	NaN	0.00	25.81	137.97	0.02	36.41	1,738.28	0.33	12.13	84.03
std	16.78	0.03	0.68	NaN	86.05	NaN	463.27	0.25	0.22	0.40	0.16	2.17	8.28	20.67	0.12	1.53	0.67	0.67	14.67	10.94	0.08	0.11	108.01	61.85	2.13	2.02	20.47	18.80	0.74	0.78	1.51	1.33	21.73	12.86	19.80	13.32	19.79	13.34	86.69	38.27	4.37	4.99	22.02	17.12	2.17	2.36	6.24	6.85	0.96	0.75	3.08	2.11	49.45	18.06	20.81	15.68	20.70	15.70	128.22	117.55	89.63	88.18	0.67	0.58	10.70	5.06	4.82	4.92	1.79	10.03	32.29	24.98	25.73	20.68	25.79	20.71	0.69	0.75	6.80	5.95	0.42	0.39	37,795.09	NaN	0.26	0.95	1.29	0.10	1.56	NaN	90.79	0.73	0.73	14.63	14.11	0.11	0.11	113.46	98.46	2.03	2.03	21.44	21.42	0.88	0.89	1.58	1.57	16.26	14.14	18.41	16.36	18.48	16.42	94.72	89.16	5.21	5.21	21.82	20.28	2.37	2.41	6.84	7.03	0.96	0.75	2.93	2.88	30.81	19.23	20.53	19.13	20.55	19.24	129.96	126.46	92.65	92.78	0.76	0.75	7.52	6.07	5.75	5.68	3.21	6.63	29.21	27.97	27.56	26.51	27.68	26.62	0.75	0.78	6.98	6.97	30.87	10.80	6.87	0.11	NaN	0.28	62.85	NaN	NaN	228.99	NaN	NaN	0.16	0.36	0.08	0.06	42.03	12.38	12.38	83.61	37,811.25	0.10	2.49	NaN	0.00	15.11	5.28	0.14	0.83	1,448.16	0.47	6.92	25.01
min	16.00	0.00	1.20	NaN	101.00	NaN	0.01	-1.00	-1.00	0.00	0.00	0.10	14.84	4.00	0.00	0.30	1.20	1.10	18.40	14.90	7.05	6.89	39.00	28.00	0.20	0.20	4.00	3.00	6.20	5.50	0.34	0.30	37.00	5.00	46.00	13.00	46.00	13.00	73.00	33.00	12.00	7.00	58.00	0.00	6.80	5.30	20.40	16.10	0.90	0.90	0.40	0.40	38.00	2.00	60.00	22.00	60.00	22.00	54.80	36.00	27.00	18.55	2.80	2.40	14.00	0.00	123.00	117.00	0.00	0.00	71.00	10.00	90.00	41.00	90.00	41.03	35.10	31.89	1.20	0.90	0.00	0.00	1.00	NaN	0.21	1.00	1.00	0.00	1.00	NaN	39.00	1.10	1.10	15.00	15.00	6.93	6.90	34.00	31.00	0.20	0.20	4.00	4.00	5.60	5.30	0.33	0.33	33.00	19.00	37.00	22.00	37.00	22.00	59.00	42.00	6.00	6.00	46.00	36.00	5.10	5.00	16.00	15.50	0.90	0.90	0.40	0.40	35.62	8.00	49.00	32.00	49.00	32.00	42.00	38.00	20.00	20.00	2.50	2.50	10.00	0.00	114.00	114.00	0.00	0.00	65.00	31.44	75.00	53.00	75.00	53.00	33.40	32.90	1.10	1.09	30.00	137.20	16.20	0.00	NaN	0.00	2.00	NaN	NaN	82.00	NaN	NaN	0.00	0.00	0.00	0.00	40.00	18.00	18.00	31.00	1.00	6.96	-24.95	NaN	0.00	4.00	117.00	0.00	32.10	0.00	0.00	0.90	38.60
25%	52.00	0.00	2.40	NaN	113.00	NaN	203.01	0.02	0.01	0.00	0.00	0.40	23.64	13.00	0.00	0.72	2.50	2.40	36.00	32.00	7.34	7.27	88.10	69.00	0.40	0.40	13.00	12.00	7.90	7.70	0.76	0.71	65.00	39.00	75.00	42.00	75.00	42.00	117.00	91.00	22.00	21.00	87.00	60.00	9.80	9.20	30.00	28.00	1.10	1.10	1.20	1.00	89.00	54.00	90.00	55.00	90.00	55.00	192.29	132.50	148.00	138.00	3.80	3.60	22.00	10.00	137.00	135.00	99.00	89.00	134.00	80.00	130.00	83.00	130.00	84.00	36.90	36.10	8.00	7.40	0.00	0.00	32,852.00	NaN	0.40	3.00	6.00	0.00	4.00	NaN	97.00	2.50	2.50	36.00	35.00	7.29	7.28	80.70	77.00	0.40	0.40	13.00	13.00	7.70	7.70	0.79	0.79	57.00	46.00	62.00	52.00	63.00	52.00	111.00	106.00	20.00	20.00	77.00	69.00	9.50	9.30	28.90	28.10	1.10	1.10	1.30	1.30	78.00	63.00	77.00	66.00	77.00	66.00	142.00	136.00	133.00	132.00	3.70	3.70	18.00	14.00	136.00	135.00	97.00	94.00	119.00	95.00	113.00	98.00	113.00	98.00	36.40	36.30	8.60	8.60	86.00	162.50	28.00	0.00	NaN	0.00	47.00	NaN	NaN	369.00	NaN	NaN	0.00	0.00	0.00	0.00	54.00	34.40	34.40	77.50	32,830.00	7.31	0.04	NaN	0.00	11.00	135.00	0.00	36.20	740.36	0.00	7.50	66.80
50%	65.00	0.00	2.90	NaN	122.00	NaN	409.02	0.05	0.02	0.00	0.00	0.60	27.65	19.00	0.00	0.98	3.00	2.90	42.80	37.00	7.39	7.34	127.00	85.00	0.60	0.60	19.00	18.00	8.40	8.20	1.00	0.95	75.00	46.00	86.00	50.00	87.00	50.00	150.00	107.00	24.00	23.00	101.00	69.00	11.40	10.90	34.50	33.20	1.30	1.21	1.90	1.50	101.00	62.00	102.00	64.00	102.00	64.00	272.67	205.00	196.00	187.00	4.20	3.90	26.00	13.00	139.00	138.00	100.00	92.00	151.00	92.00	146.00	96.00	146.00	96.00	37.11	36.40	11.00	10.10	0.00	0.00	65,665.00	NaN	0.50	4.00	6.00	0.00	5.00	NaN	133.00	3.10	3.10	42.10	41.00	7.35	7.34	120.00	107.00	0.60	0.60	18.00	18.00	8.30	8.30	1.01	1.01	66.00	55.00	74.00	62.00	74.00	62.00	140.00	134.00	23.00	23.00	90.00	82.00	11.10	11.00	33.50	33.00	1.30	1.21	2.05	2.00	90.00	74.00	90.00	78.00	90.00	79.00	223.33	214.00	181.00	181.00	4.10	4.10	21.00	16.00	139.00	138.00	99.00	96.00	136.00	112.00	131.00	115.00	130.00	115.00	36.70	36.60	12.12	12.10	104.00	170.10	33.20	0.00	NaN	0.00	109.00	NaN	NaN	504.00	NaN	NaN	0.00	0.00	0.00	0.00	67.00	40.00	40.00	103.50	65,413.00	7.36	0.14	NaN	0.00	28.00	138.00	0.00	36.50	1,386.20	0.00	10.40	80.30
75%	75.00	0.00	3.40	NaN	301.00	NaN	703.03	0.13	0.06	0.00	0.00	1.10	32.93	32.00	0.00	1.53	3.40	3.40	50.00	43.00	7.44	7.40	206.00	116.00	1.10	1.00	31.00	29.00	8.80	8.70	1.50	1.40	88.00	54.00	99.00	58.00	99.00	58.00	201.00	131.00	27.00	26.00	116.00	81.00	13.00	12.60	39.00	38.00	1.60	1.50	3.30	2.30	118.00	72.00	116.00	75.00	116.00	75.00	365.00	300.00	251.00	242.00	4.60	4.30	32.00	16.00	142.00	141.00	100.00	95.00	170.00	107.00	164.00	110.00	164.00	110.00	37.60	36.66	15.20	13.73	0.00	0.00	98,342.00	NaN	0.85	4.00	6.00	0.00	5.00	NaN	196.00	3.60	3.60	49.20	48.00	7.41	7.40	216.00	178.00	1.10	1.10	31.00	31.00	8.80	8.80	1.55	1.55	77.00	65.00	86.00	73.00	87.00	74.00	189.00	179.00	25.10	25.00	106.00	97.00	12.80	12.70	38.40	38.10	1.60	1.50	3.60	3.60	104.00	88.00	104.00	92.00	104.00	92.00	328.00	317.48	241.00	240.00	4.60	4.50	26.00	20.00	141.00	141.00	100.00	99.00	156.00	133.00	150.00	134.00	150.00	134.00	37.00	36.94	16.80	16.70	120.00	177.80	37.90	0.00	NaN	0.00	161.00	NaN	NaN	679.00	NaN	NaN	0.00	0.00	0.00	0.00	125.00	47.00	47.00	153.00	98,298.00	7.42	0.41	NaN	0.00	36.00	141.00	0.00	36.70	2,324.55	1.00	15.10	97.10
max	89.00	1.00	4.60	NaN	308.00	NaN	2,201.05	0.99	0.97	1.00	1.00	51.00	67.81	127.00	1.00	11.18	4.60	4.50	111.00	85.91	7.62	7.56	540.87	448.89	51.00	51.00	126.00	113.09	10.80	10.30	11.11	9.94	181.00	89.00	165.00	90.00	165.00	90.00	611.00	288.00	40.00	39.00	177.00	175.00	17.20	16.70	51.50	50.00	7.76	6.13	19.80	15.10	322.00	119.00	184.00	112.00	181.00	112.00	834.80	604.23	585.00	557.45	7.00	5.80	92.00	100.00	158.00	153.00	100.00	100.00	295.00	172.00	232.00	160.00	232.00	160.00	39.90	37.80	46.08	40.90	1.00	1.00	131,051.00	NaN	1.00	4.00	6.00	1.00	5.00	NaN	598.70	4.70	4.70	111.50	107.00	7.57	7.56	534.90	514.90	40.40	40.40	135.00	135.00	11.40	11.31	11.60	11.57	135.00	104.00	143.00	113.00	144.00	114.00	695.04	670.00	39.00	39.00	164.00	144.00	17.40	17.30	51.70	51.50	7.76	6.13	18.10	18.02	293.38	140.00	165.00	138.00	163.00	138.00	720.00	654.81	585.00	585.00	7.20	7.10	59.00	189.00	157.00	157.00	100.00	100.00	246.00	198.00	223.00	194.00	223.00	195.00	39.50	39.30	44.10	44.10	178.00	195.59	51.40	1.00	NaN	1.00	204.00	NaN	NaN	927.00	NaN	NaN	1.00	1.00	1.00	1.00	200.00	95.00	95.00	498.00	131,051.00	7.59	159.09	NaN	0.00	60.00	158.00	1.00	39.70	8,716.67	1.00	45.80	186.00

OverView of the dataset

train_stat2.T.head(200)

	Category	Unit of Measure	Data Type	Description	Example	count	mean	std	min	25%	50%	75%	max
age	demographic	Years	numeric	The age of the patient on unit admission	None	87,485.00	62.31	16.78	16.00	52.00	65.00	75.00	89.00
aids	APACHE comorbidity	None	binary	Whether the patient has a definitive diagnosis of acquired immune deficiency syndrome (AIDS) (not HIV positive alone)	1	90,998.00	0.00	0.03	0.00	0.00	0.00	0.00	1.00
albumin_apache	APACHE covariate	g/L	numeric	The albumin concentration measured during the first 24 hours which results in the highest APACHE III score	30	37,334.00	2.90	0.68	1.20	2.40	2.90	3.40	4.60
apache_2_bodysystem	APACHE grouping	None	string	Admission diagnosis group for APACHE II	Respiratory	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
apache_2_diagnosis	APACHE covariate	None	string	The APACHE II diagnosis for the ICU admission	308	90,051.00	185.40	86.05	101.00	113.00	122.00	301.00	308.00
apache_3j_bodysystem	APACHE grouping	None	string	Admission diagnosis group for APACHE III	Cardiovascular	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
apache_3j_diagnosis	APACHE covariate	None	string	The APACHE III-J sub-diagnosis code which best describes the reason for the ICU admission	1405	90,612.00	558.22	463.27	0.01	203.01	409.02	703.03	2,201.05
apache_4a_hospital_death_prob	APACHE prediction	None	numeric	The APACHE IVa probabilistic prediction of in-hospital mortality for the patient which utilizes the APACHE III score and other covariates, including diagnosis.	0.31	83,766.00	0.09	0.25	-1.00	0.02	0.05	0.13	0.99
apache_4a_icu_death_prob	APACHE prediction	None	numeric	The APACHE IVa probabilistic prediction of in ICU mortality for the patient which utilizes the APACHE III score and other covariates, including diagnosis	0.24	83,766.00	0.04	0.22	-1.00	0.01	0.02	0.06	0.97
apache_post_operative	APACHE covariate	None	binary	The APACHE operative status; 1 for post-operative, 0 for non-operative	1	91,713.00	0.20	0.40	0.00	0.00	0.00	0.00	1.00
arf_apache	APACHE covariate	None	binary	Whether the patient had acute renal failure during the first 24 hours of their unit stay, defined as a 24 hour urine output <410ml, creatinine >=133 micromol/L and no chronic dialysis	0	90,998.00	0.03	0.16	0.00	0.00	0.00	0.00	1.00
bilirubin_apache	APACHE covariate	micromol/L	numeric	The bilirubin concentration measured during the first 24 hours which results in the highest APACHE III score	20	33,579.00	1.15	2.17	0.10	0.40	0.60	1.10	51.00
bmi	demographic	kilograms/metres^2	string	The body mass index of the person on unit admission	21.5	88,284.00	29.19	8.28	14.84	23.64	27.65	32.93	67.81
bun_apache	APACHE covariate	mmol/L	numeric	The blood urea nitrogen concentration measured during the first 24 hours which results in the highest APACHE III score	None	72,451.00	25.83	20.67	4.00	13.00	19.00	32.00	127.00
cirrhosis	APACHE comorbidity	None	binary	Whether the patient has a history of heavy alcohol use with portal hypertension and varices, other causes of cirrhosis with evidence of portal hypertension and varices, or biopsy proven cirrhosis. This comorbidity does not apply to patients with a functioning liver transplant.	1	90,998.00	0.02	0.12	0.00	0.00	0.00	0.00	1.00
creatinine_apache	APACHE covariate	micromol/L	numeric	The creatinine concentration measured during the first 24 hours which results in the highest APACHE III score	70	72,860.00	1.48	1.53	0.30	0.72	0.98	1.53	11.18
d1_albumin_max	labs	None	numeric	The lowest albumin concentration of the patient in their serum during the first 24 hours of their unit stay	30	42,617.00	2.97	0.67	1.20	2.50	3.00	3.40	4.60
d1_albumin_min	labs	g/L	numeric	The lowest albumin concentration of the patient in their serum during the first 24 hours of their unit stay	30	42,617.00	2.90	0.67	1.10	2.40	2.90	3.40	4.50
d1_arterial_pco2_max	labs blood gas	Millimetres of mercury	numeric	The highest arterial partial pressure of carbon dioxide for the patient during the first 24 hours of their unit stay	40	32,442.00	45.25	14.67	18.40	36.00	42.80	50.00	111.00
d1_arterial_pco2_min	labs blood gas	Millimetres of mercury	numeric	The lowest arterial partial pressure of carbon dioxide for the patient during the first 24 hours of their unit stay	40	32,442.00	38.43	10.94	14.90	32.00	37.00	43.00	85.91
d1_arterial_ph_max	labs blood gas	None	numeric	The highest arterial pH for the patient during the first 24 hours of their unit stay	7.4	31,590.00	7.39	0.08	7.05	7.34	7.39	7.44	7.62
d1_arterial_ph_min	labs blood gas	None	numeric	The lowest arterial pH for the patient during the first 24 hours of their unit stay	7.4	31,590.00	7.32	0.11	6.89	7.27	7.34	7.40	7.56
d1_arterial_po2_max	labs blood gas	Millimetres of mercury	numeric	The highest arterial partial pressure of oxygen for the patient during the first 24 hours of their unit stay	80	32,451.00	165.91	108.01	39.00	88.10	127.00	206.00	540.87
d1_arterial_po2_min	labs blood gas	Millimetres of mercury	numeric	The lowest arterial partial pressure of oxygen for the patient during the first 24 hours of their unit stay	80	32,451.00	103.51	61.85	28.00	69.00	85.00	116.00	448.89
d1_bilirubin_max	labs	micromol/L	numeric	The highest bilirubin concentration of the patient in their serum or plasma during the first 24 hours of their unit stay	20	38,040.00	1.14	2.13	0.20	0.40	0.60	1.10	51.00
d1_bilirubin_min	labs	micromol/L	numeric	The lowest bilirubin concentration of the patient in their serum or plasma during the first 24 hours of their unit stay	20	38,040.00	1.07	2.02	0.20	0.40	0.60	1.00	51.00
d1_bun_max	labs	mmol/L	numeric	The highest blood urea nitrogen concentration of the patient in their serum or plasma during the first 24 hours of their unit stay	5	81,199.00	25.69	20.47	4.00	13.00	19.00	31.00	126.00
d1_bun_min	labs	mmol/L	numeric	The lowest blood urea nitrogen concentration of the patient in their serum or plasma during the first 24 hours of their unit stay	5	81,199.00	23.77	18.80	3.00	12.00	18.00	29.00	113.09
d1_calcium_max	labs	mmol/L	numeric	The highest calcium concentration of the patient in their serum during the first 24 hours of their unit stay	2.5	78,644.00	8.38	0.74	6.20	7.90	8.40	8.80	10.80
d1_calcium_min	labs	mmol/L	numeric	The lowest calcium concentration of the patient in their serum during the first 24 hours of their unit stay	2.5	78,644.00	8.18	0.78	5.50	7.70	8.20	8.70	10.30
d1_creatinine_max	labs	micromol/L	numeric	The highest creatinine concentration of the patient in their serum or plasma during the first 24 hours of their unit stay	70	81,544.00	1.49	1.51	0.34	0.76	1.00	1.50	11.11
d1_creatinine_min	labs	micromol/L	numeric	The lowest creatinine concentration of the patient in their serum or plasma during the first 24 hours of their unit stay	70	81,544.00	1.37	1.33	0.30	0.71	0.95	1.40	9.94
d1_diasbp_invasive_max	vitals	Millimetres of mercury	numeric	The patient's highest diastolic blood pressure during the first 24 hours of their unit stay, invasively measured	60	23,729.00	78.76	21.73	37.00	65.00	75.00	88.00	181.00
d1_diasbp_invasive_min	vitals	Millimetres of mercury	numeric	The patient's lowest diastolic blood pressure during the first 24 hours of their unit stay, invasively measured	60	23,729.00	46.74	12.86	5.00	39.00	46.00	54.00	89.00
d1_diasbp_max	vitals	Millimetres of mercury	numeric	The patient's highest diastolic blood pressure during the first 24 hours of their unit stay, either non-invasively or invasively measured	60	91,548.00	88.49	19.80	46.00	75.00	86.00	99.00	165.00
d1_diasbp_min	vitals	Millimetres of mercury	numeric	The patient's lowest diastolic blood pressure during the first 24 hours of their unit stay, either non-invasively or invasively measured	60	91,548.00	50.16	13.32	13.00	42.00	50.00	58.00	90.00
d1_diasbp_noninvasive_max	vitals	Millimetres of mercury	numeric	The patient's highest diastolic blood pressure during the first 24 hours of their unit stay, non-invasively measured	60	90,673.00	88.61	19.79	46.00	75.00	87.00	99.00	165.00
d1_diasbp_noninvasive_min	vitals	Millimetres of mercury	numeric	The patient's lowest diastolic blood pressure during the first 24 hours of their unit stay, non-invasively measured	60	90,673.00	50.24	13.34	13.00	42.00	50.00	58.00	90.00
d1_glucose_max	labs	mmol/L	numeric	The highest glucose concentration of the patient in their serum or plasma during the first 24 hours of their unit stay	5	85,906.00	174.64	86.69	73.00	117.00	150.00	201.00	611.00
d1_glucose_min	labs	mmol/L	numeric	The lowest glucose concentration of the patient in their serum or plasma during the first 24 hours of their unit stay	5	85,906.00	114.38	38.27	33.00	91.00	107.00	131.00	288.00
d1_hco3_max	labs	mmol/L	numeric	The highest bicarbonate concentration for the patient in their serum or plasma during the first 24 hours of their unit stay	30	76,642.00	24.37	4.37	12.00	22.00	24.00	27.00	40.00
d1_hco3_min	labs	None	numeric	The lowest bicarbonate concentration for the patient in their serum or plasma during the first 24 hours of their unit stay	30	76,642.00	23.17	4.99	7.00	21.00	23.00	26.00	39.00
d1_heartrate_max	vitals	Beats per minute	numeric	The patient's highest heart rate during the first 24 hours of their unit stay	75	91,568.00	103.00	22.02	58.00	87.00	101.00	116.00	177.00
d1_heartrate_min	vitals	Beats per minute	numeric	The patient's lowest heart rate during the first 24 hours of their unit stay	75	91,568.00	70.32	17.12	0.00	60.00	69.00	81.00	175.00
d1_hemaglobin_max	labs	g/dL	numeric	The highest hemoglobin concentration for the patient during the first 24 hours of their unit stay	10	79,566.00	11.45	2.17	6.80	9.80	11.40	13.00	17.20
d1_hemaglobin_min	labs	g/dL	numeric	The lowest hemoglobin concentration for the patient during the first 24 hours of their unit stay	10	79,566.00	10.89	2.36	5.30	9.20	10.90	12.60	16.70
d1_hematocrit_max	labs	Fraction	numeric	The highest volume proportion of red blood cells in a patient's blood during the first 24 hours of their unit stay, expressed as a fraction	0.4	80,059.00	34.53	6.24	20.40	30.00	34.50	39.00	51.50
d1_hematocrit_min	labs	Fraction	numeric	The lowest volume proportion of red blood cells in a patient's blood during the first 24 hours of their unit stay, expressed as a fraction	0.4	80,059.00	32.95	6.85	16.10	28.00	33.20	38.00	50.00
d1_inr_max	labs	micromol/L	numeric	The highest international normalized ratio for the patient during the first 24 hours of their unit stay	1	33,772.00	1.60	0.96	0.90	1.10	1.30	1.60	7.76
d1_inr_min	labs	micromol/L	numeric	The lowest international normalized ratio for the patient during the first 24 hours of their unit stay	1	33,772.00	1.48	0.75	0.90	1.10	1.21	1.50	6.13
d1_lactate_max	labs	mmol/L	numeric	The highest lactate concentration for the patient in their serum or plasma during the first 24 hours of their unit stay	1	23,317.00	2.93	3.08	0.40	1.20	1.90	3.30	19.80
d1_lactate_min	labs	mmol/L	numeric	The lowest lactate concentration for the patient in their serum or plasma during the first 24 hours of their unit stay	1	23,317.00	2.13	2.11	0.40	1.00	1.50	2.30	15.10
d1_mbp_invasive_max	vitals	Millimetres of mercury	numeric	The patient's highest mean blood pressure during the first 24 hours of their unit stay, invasively measured	80	23,936.00	114.89	49.45	38.00	89.00	101.00	118.00	322.00
d1_mbp_invasive_min	vitals	Millimetres of mercury	numeric	The patient's lowest mean blood pressure during the first 24 hours of their unit stay, invasively measured	80	23,936.00	62.32	18.06	2.00	54.00	62.00	72.00	119.00
d1_mbp_max	vitals	Millimetres of mercury	numeric	The patient's highest mean blood pressure during the first 24 hours of their unit stay, either non-invasively or invasively measured	80	91,493.00	104.65	20.81	60.00	90.00	102.00	116.00	184.00
d1_mbp_min	vitals	Millimetres of mercury	numeric	The patient's lowest mean blood pressure during the first 24 hours of their unit stay, either non-invasively or invasively measured	80	91,493.00	64.87	15.68	22.00	55.00	64.00	75.00	112.00
d1_mbp_noninvasive_max	vitals	Millimetres of mercury	numeric	The patient's highest mean blood pressure during the first 24 hours of their unit stay, non-invasively measured	80	90,234.00	104.59	20.70	60.00	90.00	102.00	116.00	181.00
d1_mbp_noninvasive_min	vitals	Millimetres of mercury	numeric	The patient's lowest mean blood pressure during the first 24 hours of their unit stay, non-invasively measured	80	90,234.00	64.94	15.70	22.00	55.00	64.00	75.00	112.00
d1_pao2fio2ratio_max	labs blood gas	Fraction	numeric	The highest fraction of inspired oxygen for the patient during the first 24 hours of their unit stay	0.21	25,705.00	285.67	128.22	54.80	192.29	272.67	365.00	834.80
d1_pao2fio2ratio_min	labs blood gas	Fraction	numeric	The lowest fraction of inspired oxygen for the patient during the first 24 hours of their unit stay	0.21	25,705.00	223.52	117.55	36.00	132.50	205.00	300.00	604.23
d1_platelets_max	labs	10^9/L	numeric	The highest platelet count for the patient during the first 24 hours of their unit stay	200	78,269.00	207.11	89.63	27.00	148.00	196.00	251.00	585.00
d1_platelets_min	labs	10^9/L	numeric	The lowest platelet count for the patient during the first 24 hours of their unit stay	200	78,269.00	196.77	88.18	18.55	138.00	187.00	242.00	557.45
d1_potassium_max	labs	mmol/L	numeric	The highest potassium concentration for the patient in their serum or plasma during the first 24 hours of their unit stay	3.8	82,128.00	4.25	0.67	2.80	3.80	4.20	4.60	7.00
d1_potassium_min	labs	mmol/L	numeric	The lowest potassium concentration for the patient in their serum or plasma during the first 24 hours of their unit stay	3.8	82,128.00	3.93	0.58	2.40	3.60	3.90	4.30	5.80
d1_resprate_max	vitals	Breaths per minute	numeric	The patient's highest respiratory rate during the first 24 hours of their unit stay	14	91,328.00	28.88	10.70	14.00	22.00	26.00	32.00	92.00
d1_resprate_min	vitals	Breaths per minute	numeric	The patient's lowest respiratory rate during the first 24 hours of their unit stay	14	91,328.00	12.85	5.06	0.00	10.00	13.00	16.00	100.00
d1_sodium_max	labs	mmol/L	numeric	The highest sodium concentration for the patient in their serum or plasma during the first 24 hours of their unit stay	145	81,518.00	139.12	4.82	123.00	137.00	139.00	142.00	158.00
d1_sodium_min	labs	mmol/L	numeric	The lowest sodium concentration for the patient in their serum or plasma during the first 24 hours of their unit stay	145	81,518.00	137.72	4.92	117.00	135.00	138.00	141.00	153.00
d1_spo2_max	vitals	Percentage	numeric	The patient's highest peripheral oxygen saturation during the first 24 hours of their unit stay	None	91,380.00	99.24	1.79	0.00	99.00	100.00	100.00	100.00
d1_spo2_min	vitals	Percentage	numeric	The patient's lowest peripheral oxygen saturation during the first 24 hours of their unit stay	100	91,380.00	90.45	10.03	0.00	89.00	92.00	95.00	100.00
d1_sysbp_invasive_max	vitals	Millimetres of mercury	numeric	The patient's highest systolic blood pressure during the first 24 hours of their unit stay, invasively measured	120	23,754.00	154.27	32.29	71.00	134.00	151.00	170.00	295.00
d1_sysbp_invasive_min	vitals	Millimetres of mercury	numeric	The patient's lowest systolic blood pressure during the first 24 hours of their unit stay, invasively measured	120	23,754.00	93.81	24.98	10.00	80.00	92.00	107.00	172.00
d1_sysbp_max	vitals	Millimetres of mercury	numeric	The patient's highest systolic blood pressure during the first 24 hours of their unit stay, either non-invasively or invasively measured	120	91,554.00	148.34	25.73	90.00	130.00	146.00	164.00	232.00
d1_sysbp_min	vitals	Millimetres of mercury	numeric	The patient's lowest systolic blood pressure during the first 24 hours of their unit stay, either non-invasively or invasively measured	120	91,554.00	96.92	20.68	41.00	83.00	96.00	110.00	160.00
d1_sysbp_noninvasive_max	vitals	Millimetres of mercury	numeric	The patient's highest systolic blood pressure during the first 24 hours of their unit stay, non-invasively measured	120	90,686.00	148.24	25.79	90.00	130.00	146.00	164.00	232.00
d1_sysbp_noninvasive_min	vitals	Millimetres of mercury	numeric	The patient's lowest systolic blood pressure during the first 24 hours of their unit stay, non-invasively measured	120	90,686.00	96.99	20.71	41.03	84.00	96.00	110.00	160.00
d1_temp_max	vitals	Degrees Celsius	numeric	The patient's highest core temperature during the first 24 hours of their unit stay, invasively measured	33	89,389.00	37.28	0.69	35.10	36.90	37.11	37.60	39.90
d1_temp_min	vitals	Degrees Celsius	numeric	The patient's lowest core temperature during the first 24 hours of their unit stay	33	89,389.00	36.27	0.75	31.89	36.10	36.40	36.66	37.80
d1_wbc_max	labs	10^9/L	numeric	The highest white blood cell count for the patient during the first 24 hours of their unit stay	10	78,539.00	12.48	6.80	1.20	8.00	11.00	15.20	46.08
d1_wbc_min	labs	10^9/L	numeric	The lowest white blood cell count for the patient during the first 24 hours of their unit stay	10	78,539.00	11.31	5.95	0.90	7.40	10.10	13.73	40.90
diabetes_mellitus	APACHE comorbidity	None	binary	Whether the patient has been diagnosed with diabetes, either juvenile or adult onset, which requires medication.	1	90,998.00	0.23	0.42	0.00	0.00	0.00	0.00	1.00
elective_surgery	demographic	None	binary	Whether the patient was admitted to the hospital for an elective surgical operation	0	91,713.00	0.18	0.39	0.00	0.00	0.00	0.00	1.00
encounter_id	identifier	None	integer	Unique identifier associated with a patient unit stay	None	91,713.00	65,606.08	37,795.09	1.00	32,852.00	65,665.00	98,342.00	131,051.00
ethnicity	demographic	None	string	The common national or cultural tradition which the person belongs to	Caucasian	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
fio2_apache	APACHE covariate	Fraction	numeric	The fraction of inspired oxygen from the arterial blood gas taken during the first 24 hours of unit admission which produces the highest APACHE III score for oxygenation	0.21	20,845.00	0.60	0.26	0.21	0.40	0.50	0.85	1.00
gcs_eyes_apache	APACHE covariate	None	integer	The eye opening component of the Glasgow Coma Scale measured during the first 24 hours which results in the highest APACHE III score	4	89,812.00	3.47	0.95	1.00	3.00	4.00	4.00	4.00
gcs_motor_apache	APACHE covariate	None	integer	The motor component of the Glasgow Coma Scale measured during the first 24 hours which results in the highest APACHE III score	6	89,812.00	5.47	1.29	1.00	6.00	6.00	6.00	6.00
gcs_unable_apache	APACHE covariate	None	binary	Whether the Glasgow Coma Scale was unable to be assessed due to patient sedation	1	90,676.00	0.01	0.10	0.00	0.00	0.00	0.00	1.00
gcs_verbal_apache	APACHE covariate	None	integer	The verbal component of the Glasgow Coma Scale measured during the first 24 hours which results in the highest APACHE III score	5	89,812.00	3.99	1.56	1.00	4.00	5.00	5.00	5.00
gender	demographic	None	string	The genotypical sex of the patient	F	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
glucose_apache	APACHE covariate	mmol/L	numeric	The glucose concentration measured during the first 24 hours which results in the highest APACHE III score	5	80,677.00	160.33	90.79	39.00	97.00	133.00	196.00	598.70
h1_albumin_max	labs	None	numeric	The lowest albumin concentration of the patient in their serum during the first hour of their unit stay	30	7,889.00	3.03	0.73	1.10	2.50	3.10	3.60	4.70
h1_albumin_min	labs	g/L	numeric	The lowest albumin concentration of the patient in their serum during the first hour of their unit stay	30	7,889.00	3.03	0.73	1.10	2.50	3.10	3.60	4.70
h1_arterial_pco2_max	labs blood gas	Millimetres of mercury	numeric	The highest arterial partial pressure of carbon dioxide for the patient during the first hour of their unit stay	40	15,754.00	44.67	14.63	15.00	36.00	42.10	49.20	111.50
h1_arterial_pco2_min	labs blood gas	Millimetres of mercury	numeric	The lowest arterial partial pressure of carbon dioxide for the patient during the first hour of their unit stay	40	15,754.00	43.38	14.11	15.00	35.00	41.00	48.00	107.00
h1_arterial_ph_max	labs blood gas	None	numeric	The highest arterial pH for the patient during the first hour of their unit stay	7.4	15,289.00	7.34	0.11	6.93	7.29	7.35	7.41	7.57
h1_arterial_ph_min	labs blood gas	None	numeric	The lowest arterial pH for the patient during the first hour of their unit stay	7.4	15,289.00	7.33	0.11	6.90	7.28	7.34	7.40	7.56
h1_arterial_po2_max	labs blood gas	Millimetres of mercury	numeric	The highest arterial partial pressure of oxygen for the patient during the first hour of their unit stay	80	15,768.00	163.84	113.46	34.00	80.70	120.00	216.00	534.90
h1_arterial_po2_min	labs blood gas	Millimetres of mercury	numeric	The lowest arterial partial pressure of oxygen for the patient during the first hour of their unit stay	80	15,768.00	144.15	98.46	31.00	77.00	107.00	178.00	514.90
h1_bilirubin_max	labs	micromol/L	numeric	The highest bilirubin concentration of the patient in their serum or plasma during the first hour of their unit stay	20	7,094.00	1.10	2.03	0.20	0.40	0.60	1.10	40.40
h1_bilirubin_min	labs	micromol/L	numeric	The lowest bilirubin concentration of the patient in their serum or plasma during the first hour of their unit stay	20	7,094.00	1.10	2.03	0.20	0.40	0.60	1.10	40.40
h1_bun_max	labs	mmol/L	numeric	The highest blood urea nitrogen concentration of the patient in their serum or plasma during the first hour of their unit stay	5	16,622.00	25.84	21.44	4.00	13.00	18.00	31.00	135.00
h1_bun_min	labs	mmol/L	numeric	The lowest blood urea nitrogen concentration of the patient in their serum or plasma during the first hour of their unit stay	5	16,622.00	25.82	21.42	4.00	13.00	18.00	31.00	135.00
h1_calcium_max	labs	mmol/L	numeric	The highest calcium concentration of the patient in their serum during the first hour of their unit stay	2.5	15,850.00	8.28	0.88	5.60	7.70	8.30	8.80	11.40
h1_calcium_min	labs	mmol/L	numeric	The lowest calcium concentration of the patient in their serum during the first hour of their unit stay	2.5	15,850.00	8.28	0.89	5.30	7.70	8.30	8.80	11.31
h1_creatinine_max	labs	micromol/L	numeric	The highest creatinine concentration of the patient in their serum or plasma during the first hour of their unit stay	70	16,756.00	1.53	1.58	0.33	0.79	1.01	1.55	11.60
h1_creatinine_min	labs	micromol/L	numeric	The lowest creatinine concentration of the patient in their serum or plasma during the first hour of their unit stay	70	16,756.00	1.53	1.57	0.33	0.79	1.01	1.55	11.57
h1_diasbp_invasive_max	vitals	Millimetres of mercury	numeric	The patient's highest diastolic blood pressure during the first hour of their unit stay, invasively measured	60	16,785.00	67.97	16.26	33.00	57.00	66.00	77.00	135.00
h1_diasbp_invasive_min	vitals	Millimetres of mercury	numeric	The patient's lowest diastolic blood pressure during the first hour of their unit stay, invasively measured	60	16,785.00	56.14	14.14	19.00	46.00	55.00	65.00	104.00
h1_diasbp_max	vitals	Millimetres of mercury	numeric	The patient's highest diastolic blood pressure during the first hour of their unit stay, either non-invasively or invasively measured	60	88,094.00	75.35	18.41	37.00	62.00	74.00	86.00	143.00
h1_diasbp_min	vitals	Millimetres of mercury	numeric	The patient's lowest diastolic blood pressure during the first hour of their unit stay, either non-invasively or invasively measured	60	88,094.00	62.84	16.36	22.00	52.00	62.00	73.00	113.00
h1_diasbp_noninvasive_max	vitals	Millimetres of mercury	numeric	The patient's highest diastolic blood pressure during the first hour of their unit stay, non-invasively measured	60	84,363.00	75.81	18.48	37.00	63.00	74.00	87.00	144.00
h1_diasbp_noninvasive_min	vitals	Millimetres of mercury	numeric	The patient's lowest diastolic blood pressure during the first hour of their unit stay, non-invasively measured	60	84,363.00	63.27	16.42	22.00	52.00	62.00	74.00	114.00
h1_glucose_max	labs	mmol/L	numeric	The highest glucose concentration of the patient in their serum or plasma during the first hour of their unit stay	5	39,099.00	167.99	94.72	59.00	111.00	140.00	189.00	695.04
h1_glucose_min	labs	mmol/L	numeric	The lowest glucose concentration of the patient in their serum or plasma during the first hour of their unit stay	5	39,099.00	159.22	89.16	42.00	106.00	134.00	179.00	670.00
h1_hco3_max	labs	mmol/L	numeric	The highest bicarbonate concentration for the patient in their serum or plasma during the first hour of their unit stay	30	15,619.00	22.50	5.21	6.00	20.00	23.00	25.10	39.00
h1_hco3_min	labs	None	numeric	The lowest bicarbonate concentration for the patient in their serum or plasma during the first hour of their unit stay	30	15,619.00	22.42	5.21	6.00	20.00	23.00	25.00	39.00
h1_heartrate_max	vitals	Beats per minute	numeric	The patient's highest heart rate during the first hour of their unit stay	75	88,923.00	92.23	21.82	46.00	77.00	90.00	106.00	164.00
h1_heartrate_min	vitals	Beats per minute	numeric	The patient's lowest heart rate during the first hour of their unit stay	75	88,923.00	83.66	20.28	36.00	69.00	82.00	97.00	144.00
h1_hemaglobin_max	labs	g/dL	numeric	The highest hemoglobin concentration for the patient during the first hour of their unit stay	10	18,590.00	11.19	2.37	5.10	9.50	11.10	12.80	17.40
h1_hemaglobin_min	labs	g/dL	numeric	The lowest hemoglobin concentration for the patient during the first hour of their unit stay	10	18,590.00	11.04	2.41	5.00	9.30	11.00	12.70	17.30
h1_hematocrit_max	labs	Fraction	numeric	The highest volume proportion of red blood cells in a patient's blood during the first hour of their unit stay, expressed as a fraction	0.4	18,293.00	33.67	6.84	16.00	28.90	33.50	38.40	51.70
h1_hematocrit_min	labs	Fraction	numeric	The lowest volume proportion of red blood cells in a patient's blood during the first hour of their unit stay, expressed as a fraction	0.4	18,293.00	33.22	7.03	15.50	28.10	33.00	38.10	51.50
h1_inr_max	labs	micromol/L	numeric	The highest international normalized ratio for the patient during the first hour of their unit stay	1	33,772.00	1.60	0.96	0.90	1.10	1.30	1.60	7.76
h1_inr_min	labs	micromol/L	numeric	The lowest international normalized ratio for the patient during the first hour of their unit stay	1	33,772.00	1.48	0.75	0.90	1.10	1.21	1.50	6.13
h1_lactate_max	labs	mmol/L	numeric	The highest lactate concentration for the patient in their serum or plasma during the first hour of their unit stay	1	7,344.00	3.07	2.93	0.40	1.30	2.05	3.60	18.10
h1_lactate_min	labs	mmol/L	numeric	The lowest lactate concentration for the patient in their serum or plasma during the first hour of their unit stay	1	7,344.00	3.02	2.88	0.40	1.30	2.00	3.60	18.02
h1_mbp_invasive_max	vitals	Millimetres of mercury	numeric	The patient's highest mean blood pressure during the first hour of their unit stay, invasively measured	80	16,869.00	94.88	30.81	35.62	78.00	90.00	104.00	293.38
h1_mbp_invasive_min	vitals	Millimetres of mercury	numeric	The patient's lowest mean blood pressure during the first hour of their unit stay, invasively measured	80	16,869.00	75.97	19.23	8.00	63.00	74.00	88.00	140.00
h1_mbp_max	vitals	Millimetres of mercury	numeric	The patient's highest mean blood pressure during the first hour of their unit stay, either non-invasively or invasively measured	80	87,074.00	91.61	20.53	49.00	77.00	90.00	104.00	165.00
h1_mbp_min	vitals	Millimetres of mercury	numeric	The patient's lowest mean blood pressure during the first hour of their unit stay, either non-invasively or invasively measured	80	87,074.00	79.40	19.13	32.00	66.00	78.00	92.00	138.00
h1_mbp_noninvasive_max	vitals	Millimetres of mercury	numeric	The patient's highest mean blood pressure during the first hour of their unit stay, non-invasively measured	80	82,629.00	91.59	20.55	49.00	77.00	90.00	104.00	163.00
h1_mbp_noninvasive_min	vitals	Millimetres of mercury	numeric	The patient's lowest mean blood pressure during the first hour of their unit stay, non-invasively measured	80	82,629.00	79.71	19.24	32.00	66.00	79.00	92.00	138.00
h1_pao2fio2ratio_max	labs blood gas	Fraction	numeric	The highest fraction of inspired oxygen for the patient during the first hour of their unit stay	0.21	11,518.00	244.40	129.96	42.00	142.00	223.33	328.00	720.00
h1_pao2fio2ratio_min	labs blood gas	Fraction	numeric	The lowest fraction of inspired oxygen for the patient during the first hour of their unit stay	0.21	11,518.00	235.93	126.46	38.00	136.00	214.00	317.48	654.81
h1_platelets_max	labs	10^9/L	numeric	The highest platelet count for the patient during the first hour of their unit stay	200	16,040.00	196.10	92.65	20.00	133.00	181.00	241.00	585.00
h1_platelets_min	labs	10^9/L	numeric	The lowest platelet count for the patient during the first hour of their unit stay	200	16,040.00	195.48	92.78	20.00	132.00	181.00	240.00	585.00
h1_potassium_max	labs	mmol/L	numeric	The highest potassium concentration for the patient in their serum or plasma during the first hour of their unit stay	3.8	19,611.00	4.20	0.76	2.50	3.70	4.10	4.60	7.20
h1_potassium_min	labs	mmol/L	numeric	The lowest potassium concentration for the patient in their serum or plasma during the first hour of their unit stay	3.8	19,611.00	4.15	0.75	2.50	3.70	4.10	4.50	7.10
h1_resprate_max	vitals	Breaths per minute	numeric	The patient's highest respiratory rate during the first hour of their unit stay	14	87,356.00	22.63	7.52	10.00	18.00	21.00	26.00	59.00
h1_resprate_min	vitals	Breaths per minute	numeric	The patient's lowest respiratory rate during the first hour of their unit stay	14	87,356.00	17.21	6.07	0.00	14.00	16.00	20.00	189.00
h1_sodium_max	labs	mmol/L	numeric	The highest sodium concentration for the patient in their serum or plasma during the first hour of their unit stay	145	19,096.00	138.24	5.75	114.00	136.00	139.00	141.00	157.00
h1_sodium_min	labs	mmol/L	numeric	The lowest sodium concentration for the patient in their serum or plasma during the first hour of their unit stay	145	19,096.00	137.90	5.68	114.00	135.00	138.00	141.00	157.00
h1_spo2_max	vitals	Percentage	numeric	The patient's highest peripheral oxygen saturation during the first hour of their unit stay	None	87,528.00	98.04	3.21	0.00	97.00	99.00	100.00	100.00
h1_spo2_min	vitals	Percentage	numeric	The patient's lowest peripheral oxygen saturation during the first hour of their unit stay	100	87,528.00	95.17	6.63	0.00	94.00	96.00	99.00	100.00
h1_sysbp_invasive_max	vitals	Millimetres of mercury	numeric	The patient's highest systolic blood pressure during the first hour of their unit stay, invasively measured	120	16,798.00	138.70	29.21	65.00	119.00	136.00	156.00	246.00
h1_sysbp_invasive_min	vitals	Millimetres of mercury	numeric	The patient's lowest systolic blood pressure during the first hour of their unit stay, invasively measured	120	16,798.00	114.83	27.97	31.44	95.00	112.00	133.00	198.00
h1_sysbp_max	vitals	Millimetres of mercury	numeric	The patient's highest systolic blood pressure during the first hour of their unit stay, either non-invasively or invasively measured	120	88,102.00	133.25	27.56	75.00	113.00	131.00	150.00	223.00
h1_sysbp_min	vitals	Millimetres of mercury	numeric	The patient's lowest systolic blood pressure during the first hour of their unit stay, either non-invasively or invasively measured	120	88,102.00	116.36	26.51	53.00	98.00	115.00	134.00	194.00
h1_sysbp_noninvasive_max	vitals	Millimetres of mercury	numeric	The patient's highest systolic blood pressure during the first hour of their unit stay, non-invasively measured	120	84,372.00	133.05	27.68	75.00	113.00	130.00	150.00	223.00
h1_sysbp_noninvasive_min	vitals	Millimetres of mercury	numeric	The patient's lowest systolic blood pressure during the first hour of their unit stay, non-invasively measured	120	84,372.00	116.55	26.62	53.00	98.00	115.00	134.00	195.00
h1_temp_max	vitals	Degrees Celsius	numeric	The patient's highest core temperature during the first hour of their unit stay, invasively measured	33	69,981.00	36.71	0.75	33.40	36.40	36.70	37.00	39.50
h1_temp_min	vitals	Degrees Celsius	numeric	The patient's lowest core temperature during the first hour of their unit stay	33	69,981.00	36.61	0.78	32.90	36.30	36.60	36.94	39.30
h1_wbc_max	labs	10^9/L	numeric	The highest white blood cell count for the patient during the first hour of their unit stay	10	15,760.00	13.46	6.98	1.10	8.60	12.12	16.80	44.10
h1_wbc_min	labs	10^9/L	numeric	The lowest white blood cell count for the patient during the first hour of their unit stay	10	15,760.00	13.42	6.97	1.09	8.60	12.10	16.70	44.10
heart_rate_apache	APACHE covariate	Beats per minute	numeric	The heart rate measured during the first 24 hours which results in the highest APACHE III score	75	90,835.00	99.71	30.87	30.00	86.00	104.00	120.00	178.00
height	demographic	centimetres	numeric	The height of the person on unit admission	180	90,379.00	169.64	10.80	137.20	162.50	170.10	177.80	195.59
hematocrit_apache	APACHE covariate	Fraction	numeric	The hematocrit measured during the first 24 hours which results in the highest APACHE III score	0.4	71,835.00	32.99	6.87	16.20	28.00	33.20	37.90	51.40
hepatic_failure	APACHE comorbidity	None	binary	Whether the patient has cirrhosis and additional complications including jaundice and ascites, upper GI bleeding, hepatic encephalopathy, or coma.	1	90,998.00	0.01	0.11	0.00	0.00	0.00	0.00	1.00
hospital_admit_source	demographic	None	string	The location of the patient prior to being admitted to the hospital	Home	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
hospital_death	demographic	None	binary	Whether the patient died during this hospitalization	0	91,713.00	0.09	0.28	0.00	0.00	0.00	0.00	1.00
hospital_id	identifier	None	integer	Unique identifier associated with a hospital	None	91,713.00	105.67	62.85	2.00	47.00	109.00	161.00	204.00
icu_admit_source	demographic	None	string	The location of the patient prior to being admitted to the unit	Operating room	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
icu_admit_type	demographic	None	string	The type of unit admission for the patient	Cardiothoracic	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
icu_id	demographic	None	integer	A unique identifier for the unit to which the patient was admitted	None	91,713.00	508.36	228.99	82.00	369.00	504.00	679.00	927.00
icu_stay_type	demographic	None	string	NaN	None	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
icu_type	demographic	None	string	A classification which indicates the type of care the unit is capable of providing	Neurological ICU	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
immunosuppression	APACHE comorbidity	None	binary	Whether the patient has their immune system suppressed within six months prior to ICU admission for any of the following reasons; radiation therapy, chemotherapy, use of non-cytotoxic immunosuppressive drugs, high dose steroids (at least 0.3 mg/kg/day of methylprednisolone or equivalent for at least 6 months).	1	90,998.00	0.03	0.16	0.00	0.00	0.00	0.00	1.00
intubated_apache	APACHE covariate	None	binary	Whether the patient was intubated at the time of the highest scoring arterial blood gas used in the oxygenation score	0	90,998.00	0.15	0.36	0.00	0.00	0.00	0.00	1.00
leukemia	APACHE comorbidity	None	binary	Whether the patient has been diagnosed with acute or chronic myelogenous leukemia, acute or chronic lymphocytic leukemia, or multiple myeloma.	1	90,998.00	0.01	0.08	0.00	0.00	0.00	0.00	1.00
lymphoma	APACHE comorbidity	None	binary	Whether the patient has been diagnosed with non-Hodgkin lymphoma.	1	90,998.00	0.00	0.06	0.00	0.00	0.00	0.00	1.00
map_apache	APACHE covariate	Millimetres of mercury	numeric	The mean arterial pressure measured during the first 24 hours which results in the highest APACHE III score	None	90,719.00	88.02	42.03	40.00	54.00	67.00	125.00	200.00
paco2_apache	APACHE covariate	Millimetres of mercury	numeric	The partial pressure of carbon dioxide from the arterial blood gas taken during the first 24 hours of unit admission which produces the highest APACHE III score for oxygenation	40	20,845.00	42.18	12.38	18.00	34.40	40.00	47.00	95.00
paco2_for_ph_apache	APACHE covariate	Millimetres of mercury	numeric	The partial pressure of carbon dioxide from the arterial blood gas taken during the first 24 hours of unit admission which produces the highest APACHE III score for acid-base disturbance	40	20,845.00	42.18	12.38	18.00	34.40	40.00	47.00	95.00
pao2_apache	APACHE covariate	Millimetres of mercury	numeric	The partial pressure of oxygen from the arterial blood gas taken during the first 24 hours of unit admission which produces the highest APACHE III score for oxygenation	80	20,845.00	131.15	83.61	31.00	77.50	103.50	153.00	498.00
patient_id	identifier	None	integer	Unique identifier associated with a patient	None	91,713.00	65,537.13	37,811.25	1.00	32,830.00	65,413.00	98,298.00	131,051.00
ph_apache	APACHE covariate	None	numeric	The pH from the arterial blood gas taken during the first 24 hours of unit admission which produces the highest APACHE III score for acid-base disturbance	7.4	20,845.00	7.35	0.10	6.96	7.31	7.36	7.42	7.59
pre_icu_los_days	demographic	Days	numeric	The length of stay of the patient between hospital admission and unit admission	3.5	91,713.00	0.84	2.49	-24.95	0.04	0.14	0.41	159.09
pred	GOSSIS example prediction	None	numeric	Example mortality prediction, shared as a 'baseline' based on one of the GOSSIS algorithm development models.	0.000921	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
readmission_status	demographic	None	binary	Whether the current unit stay is the second (or greater) stay at an ICU within the same hospitalization	0	91,713.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
resprate_apache	APACHE covariate	Breaths per minute	numeric	The respiratory rate measured during the first 24 hours which results in the highest APACHE III score	14	90,479.00	25.81	15.11	4.00	11.00	28.00	36.00	60.00
sodium_apache	APACHE covariate	mmol/L	numeric	The sodium concentration measured during the first 24 hours which results in the highest APACHE III score	145	73,113.00	137.97	5.28	117.00	135.00	138.00	141.00	158.00
solid_tumor_with_metastasis	APACHE comorbidity	None	binary	Whether the patient has been diagnosed with any solid tumor carcinoma (including malignant melanoma) which has evidence of metastasis.	1	90,998.00	0.02	0.14	0.00	0.00	0.00	0.00	1.00
temp_apache	APACHE covariate	Degrees Celsius	numeric	The temperature measured during the first 24 hours which results in the highest APACHE III score	33	87,605.00	36.41	0.83	32.10	36.20	36.50	36.70	39.70
urineoutput_apache	APACHE covariate	Millilitres	numeric	The total urine output for the first 24 hours	2000	42,715.00	1,738.28	1,448.16	0.00	740.36	1,386.20	2,324.55	8,716.67
ventilated_apache	APACHE covariate	None	binary	Whether the patient was invasively ventilated at the time of the highest scoring arterial blood gas using the oxygenation scoring algorithm, including any mode of positive pressure ventilation delivered through a circuit attached to an endo-tracheal tube or tracheostomy	1	90,998.00	0.33	0.47	0.00	0.00	0.00	1.00	1.00
wbc_apache	APACHE covariate	10^9/L	numeric	The white blood cell count measured during the first 24 hours which results in the highest APACHE III score	10	69,701.00	12.13	6.92	0.90	7.50	10.40	15.10	45.80
weight	demographic	kilograms	numeric	The weight (body mass) of the person on unit admission	80	88,993.00	84.03	25.01	38.60	66.80	80.30	97.10	186.00

EDA

# Missing Values
train.isna().sum()

encounter_id                     0    
patient_id                       0    
hospital_id                      0    
hospital_death                   0    
age                              4228 
bmi                              3429 
elective_surgery                 0    
ethnicity                        1395 
gender                           25   
height                           1334 
hospital_admit_source            21409
icu_admit_source                 112  
icu_id                           0    
icu_stay_type                    0    
icu_type                         0    
pre_icu_los_days                 0    
readmission_status               0    
weight                           2720 
albumin_apache                   54379
apache_2_diagnosis               1662 
apache_3j_diagnosis              1101 
apache_post_operative            0    
arf_apache                       715  
bilirubin_apache                 58134
bun_apache                       19262
creatinine_apache                18853
fio2_apache                      70868
gcs_eyes_apache                  1901 
gcs_motor_apache                 1901 
gcs_unable_apache                1037 
gcs_verbal_apache                1901 
glucose_apache                   11036
heart_rate_apache                878  
hematocrit_apache                19878
intubated_apache                 715  
map_apache                       994  
paco2_apache                     70868
paco2_for_ph_apache              70868
pao2_apache                      70868
ph_apache                        70868
resprate_apache                  1234 
sodium_apache                    18600
temp_apache                      4108 
urineoutput_apache               48998
ventilated_apache                715  
wbc_apache                       22012
d1_diasbp_invasive_max           67984
d1_diasbp_invasive_min           67984
d1_diasbp_max                    165  
d1_diasbp_min                    165  
d1_diasbp_noninvasive_max        1040 
d1_diasbp_noninvasive_min        1040 
d1_heartrate_max                 145  
d1_heartrate_min                 145  
d1_mbp_invasive_max              67777
d1_mbp_invasive_min              67777
d1_mbp_max                       220  
d1_mbp_min                       220  
d1_mbp_noninvasive_max           1479 
d1_mbp_noninvasive_min           1479 
d1_resprate_max                  385  
d1_resprate_min                  385  
d1_spo2_max                      333  
d1_spo2_min                      333  
d1_sysbp_invasive_max            67959
d1_sysbp_invasive_min            67959
d1_sysbp_max                     159  
d1_sysbp_min                     159  
d1_sysbp_noninvasive_max         1027 
d1_sysbp_noninvasive_min         1027 
d1_temp_max                      2324 
d1_temp_min                      2324 
h1_diasbp_invasive_max           74928
h1_diasbp_invasive_min           74928
h1_diasbp_max                    3619 
h1_diasbp_min                    3619 
h1_diasbp_noninvasive_max        7350 
h1_diasbp_noninvasive_min        7350 
h1_heartrate_max                 2790 
h1_heartrate_min                 2790 
h1_mbp_invasive_max              74844
h1_mbp_invasive_min              74844
h1_mbp_max                       4639 
h1_mbp_min                       4639 
h1_mbp_noninvasive_max           9084 
h1_mbp_noninvasive_min           9084 
h1_resprate_max                  4357 
h1_resprate_min                  4357 
h1_spo2_max                      4185 
h1_spo2_min                      4185 
h1_sysbp_invasive_max            74915
h1_sysbp_invasive_min            74915
h1_sysbp_max                     3611 
h1_sysbp_min                     3611 
h1_sysbp_noninvasive_max         7341 
h1_sysbp_noninvasive_min         7341 
h1_temp_max                      21732
h1_temp_min                      21732
d1_albumin_max                   49096
d1_albumin_min                   49096
d1_bilirubin_max                 53673
d1_bilirubin_min                 53673
d1_bun_max                       10514
d1_bun_min                       10514
d1_calcium_max                   13069
d1_calcium_min                   13069
d1_creatinine_max                10169
d1_creatinine_min                10169
d1_glucose_max                   5807 
d1_glucose_min                   5807 
d1_hco3_max                      15071
d1_hco3_min                      15071
d1_hemaglobin_max                12147
d1_hemaglobin_min                12147
d1_hematocrit_max                11654
d1_hematocrit_min                11654
d1_inr_max                       57941
d1_inr_min                       57941
d1_lactate_max                   68396
d1_lactate_min                   68396
d1_platelets_max                 13444
d1_platelets_min                 13444
d1_potassium_max                 9585 
d1_potassium_min                 9585 
d1_sodium_max                    10195
d1_sodium_min                    10195
d1_wbc_max                       13174
d1_wbc_min                       13174
h1_albumin_max                   83824
h1_albumin_min                   83824
h1_bilirubin_max                 84619
h1_bilirubin_min                 84619
h1_bun_max                       75091
h1_bun_min                       75091
h1_calcium_max                   75863
h1_calcium_min                   75863
h1_creatinine_max                74957
h1_creatinine_min                74957
h1_glucose_max                   52614
h1_glucose_min                   52614
h1_hco3_max                      76094
h1_hco3_min                      76094
h1_hemaglobin_max                73123
h1_hemaglobin_min                73123
h1_hematocrit_max                73420
h1_hematocrit_min                73420
h1_inr_max                       57941
h1_inr_min                       57941
h1_lactate_max                   84369
h1_lactate_min                   84369
h1_platelets_max                 75673
h1_platelets_min                 75673
h1_potassium_max                 72102
h1_potassium_min                 72102
h1_sodium_max                    72617
h1_sodium_min                    72617
h1_wbc_max                       75953
h1_wbc_min                       75953
d1_arterial_pco2_max             59271
d1_arterial_pco2_min             59271
d1_arterial_ph_max               60123
d1_arterial_ph_min               60123
d1_arterial_po2_max              59262
d1_arterial_po2_min              59262
d1_pao2fio2ratio_max             66008
d1_pao2fio2ratio_min             66008
h1_arterial_pco2_max             75959
h1_arterial_pco2_min             75959
h1_arterial_ph_max               76424
h1_arterial_ph_min               76424
h1_arterial_po2_max              75945
h1_arterial_po2_min              75945
h1_pao2fio2ratio_max             80195
h1_pao2fio2ratio_min             80195
apache_4a_hospital_death_prob    7947 
apache_4a_icu_death_prob         7947 
aids                             715  
cirrhosis                        715  
diabetes_mellitus                715  
hepatic_failure                  715  
immunosuppression                715  
leukemia                         715  
lymphoma                         715  
solid_tumor_with_metastasis      715  
apache_3j_bodysystem             1662 
apache_2_bodysystem              1662 
dtype: int64

Functions

# function to evaluate the score of our model
def eval_auc(pred,real):
    false_positive_rate, recall, thresholds = roc_curve(real, pred)
    roc_auc = auc(false_positive_rate, recall)
    return roc_auc    

# a wrapper class  that we can have the same ouput whatever the model we choose
class Base_Model(object):
    
    def __init__(self, train_df, test_df, features, categoricals=[], n_splits=5, verbose=True,ps={}):
        self.train_df = train_df
        self.test_df = test_df
        self.features = features
        self.n_splits = n_splits
        self.categoricals = categoricals
        self.target = 'hospital_death'
        self.cv = self.get_cv()
        self.verbose = verbose
#         self.params = self.get_params()
        self.params = self.set_params(ps)
        self.y_pred, self.score, self.model , self.oof_pred = self.fit()
        
    def train_model(self, train_set, val_set):
        raise NotImplementedError
        
    def get_cv(self):
        cv = StratifiedKFold(n_splits=self.n_splits, shuffle=True, random_state=42)
        return cv.split(self.train_df, self.train_df[self.target])
    
    def get_params(self):
        raise NotImplementedError
        
    def convert_dataset(self, x_train, y_train, x_val, y_val):
        raise NotImplementedError
        
    def convert_x(self, x):
        return x
        
    def fit(self):
        oof_pred = np.zeros((len(self.train_df), ))
        y_pred = np.zeros((len(self.test_df), ))
        for fold, (train_idx, val_idx) in enumerate(self.cv):
            x_train, x_val = self.train_df[self.features].iloc[train_idx], self.train_df[self.features].iloc[val_idx]
            y_train, y_val = self.train_df[self.target][train_idx], self.train_df[self.target][val_idx]
            train_set, val_set = self.convert_dataset(x_train, y_train, x_val, y_val)
            model = self.train_model(train_set, val_set)
            conv_x_val = self.convert_x(x_val)
            oof_pred[val_idx] = model.predict(conv_x_val).reshape(oof_pred[val_idx].shape)
            x_test = self.convert_x(self.test_df[self.features])
            y_pred += model.predict(x_test).reshape(y_pred.shape) / self.n_splits

            print('Partial score of fold {} is: {}'.format(fold,eval_auc(oof_pred[val_idx],y_val) ))
        #print(oof_pred, self.train_df[self.target].values)
        loss_score = eval_auc(oof_pred,self.train_df[self.target].values) 
        if self.verbose:
            print('Our oof AUC score is: ', loss_score)
        return y_pred, loss_score, model , oof_pred

#we choose to try a LightGbM using the Base_Model class
class Lgb_Model(Base_Model):
    
    def train_model(self, train_set, val_set):
        verbosity = 100 if self.verbose else 0
        return lgb.train(self.params, train_set, valid_sets=[train_set, val_set], verbose_eval=verbosity)
        
    def convert_dataset(self, x_train, y_train, x_val, y_val):
        train_set = lgb.Dataset(x_train, y_train, categorical_feature=self.categoricals)
        val_set   = lgb.Dataset(x_val,    y_val,  categorical_feature=self.categoricals)
        return train_set, val_set
        
    def get_params(self):
        params = {'n_estimators':5000,
                    'boosting_type': 'gbdt',
                    'objective': 'binary',
                    'metric': 'auc',
                    'subsample': 0.75,
                    'subsample_freq': 1,
                    'learning_rate': 0.1,
                    'feature_fraction': 0.9,
                    'max_depth': 15,
                    'lambda_l1': 1,  
                    'lambda_l2': 1,
                    'early_stopping_rounds': 100,
                    #'is_unbalance' : True ,
                    'scale_pos_weight' : 3
                  
                    }
        return params
    def set_params(self,ps={}):
        params = self.get_params()
        if 'subsample_freq' in ps:
            params['subsample_freq']=int(ps['subsample_freq'])
            params['learning_rate']=ps['learning_rate']
            params['feature_fraction']=ps['feature_fraction']
            params['lambda_l1']=ps['lambda_l1']
            params['lambda_l2']=ps['lambda_l2']
            params['scale_pos_weight']=ps['scale_pos_weight']
            params['max_depth']=int(ps['max_depth'])
        
        return params  

Feature Engineering

#we are going to drop these columns because we dont want our ML model to be bias toward these consideration
#(we also remove the target and the ids.)
to_drop = ['gender','ethnicity' ,'encounter_id', 'patient_id',  'hospital_death']

# this is a list of features that look like to be categorical
categoricals_features = ['hospital_id','ethnicity','gender','hospital_admit_source','icu_admit_source',
                         'icu_stay_type','icu_type','apache_3j_bodysystem','apache_2_bodysystem']
categoricals_features = [col for col in categoricals_features if col not in to_drop]

# this is the list of all input feature we would like our model to use 
features = [col for col in train.columns if col not in to_drop ]
print('numerber of features ' , len(features))
print('shape of train / test ', train.shape , test.shape)

numerber of features  181
shape of train / test  (91713, 186) (39308, 186)

categorical feature need to be transform to numeric for mathematical purpose. different technics of categorical encoding exists here we will rely on our model API to deal with categorical still we need to encode each categorical value to an id , for this purpose we use LabelEncoder

# categorical feature need to be transform to numeric for mathematical purpose.
# different technics of categorical encoding exists here we will rely on our model API to deal with categorical
# still we need to encode each categorical value to an id , for this purpose we use LabelEncoder

print('Transform all String features to category.\n')
for usecol in categoricals_features:
    train[usecol] = train[usecol].astype('str')
    test[usecol] = test[usecol].astype('str')
    
    #Fit LabelEncoder
    le = LabelEncoder().fit(
            np.unique(train[usecol].unique().tolist()+
                      test[usecol].unique().tolist()))

    #At the end 0 will be used for dropped values
    train[usecol] = le.transform(train[usecol])+1
    test[usecol]  = le.transform(test[usecol])+1
    
    train[usecol] = train[usecol].replace(np.nan, 0).astype('int').astype('category')
    test[usecol]  = test[usecol].replace(np.nan, 0).astype('int').astype('category')

Transform all String features to category.

# Drop all missing Values # obs: # we delete a particular row if it has a null value for a particular feature. # This method is used only when there are enough samples in the data set. # It has to be ensured that there is no bias after data deletion. # Removing the data will lead to loss of information which will not give the expected results while predicting # the output. print('Train Dataset: ') print("Orginal shape before dropna()" ,train.shape) train = train.dropna() print("Shape after dropna()" ,train.shape) print('\n\n') print('Test Dataset: ') print("Orginal shape before dropna()" ,test.shape) test = test.dropna() print("Shape after dropna()" ,test.shape)

# Drop the values above a certain threshold
# If the information contained in the variable is not that high, you can drop the variable 
# if it has more than 50% missing values. In this method we are dropping columns with null values above a 
# certain threshold

threshold = len(train) * 0.60

df_train_thresh = train.dropna(axis=1, thresh=threshold)

# View columns in the dataset
display(df_train_thresh.shape)

print('Columns that were removed:')
list(set(train.columns) - set(df_train_thresh.columns))

(91713, 112)

Columns that were removed:

['d1_sysbp_invasive_max', 'd1_arterial_ph_max', 'h1_pao2fio2ratio_max', 'd1_arterial_po2_min', 'h1_inr_max', 'h1_sodium_max', 'd1_albumin_max', 'd1_arterial_pco2_min', 'pao2_apache', 'h1_platelets_min', 'urineoutput_apache', 'd1_arterial_pco2_max', 'h1_calcium_min', 'h1_sysbp_invasive_min', 'h1_calcium_max', 'h1_hemaglobin_min', 'd1_bilirubin_max', 'd1_pao2fio2ratio_max', 'h1_albumin_max', 'h1_pao2fio2ratio_min', 'h1_wbc_max', 'paco2_apache', 'd1_arterial_ph_min', 'h1_mbp_invasive_max', 'h1_sodium_min', 'h1_creatinine_min', 'h1_hematocrit_min', 'h1_diasbp_invasive_max', 'd1_bilirubin_min', 'd1_inr_max', 'h1_mbp_invasive_min', 'h1_albumin_min', 'd1_mbp_invasive_min', 'h1_potassium_max', 'h1_sysbp_invasive_max', 'h1_lactate_min', 'd1_mbp_invasive_max', 'h1_lactate_max', 'h1_inr_min', 'h1_hemaglobin_max', 'd1_diasbp_invasive_max', 'h1_arterial_pco2_min', 'ph_apache', 'h1_bun_max', 'h1_creatinine_max', 'd1_sysbp_invasive_min', 'albumin_apache', 'd1_inr_min', 'h1_arterial_po2_min', 'h1_glucose_min', 'h1_glucose_max', 'h1_bun_min', 'd1_arterial_po2_max', 'h1_arterial_ph_max', 'bilirubin_apache', 'h1_hco3_min', 'd1_diasbp_invasive_min', 'd1_lactate_min', 'd1_lactate_max', 'h1_arterial_ph_min', 'h1_diasbp_invasive_min', 'fio2_apache', 'paco2_for_ph_apache', 'h1_platelets_max', 'h1_wbc_min', 'd1_pao2fio2ratio_min', 'h1_bilirubin_min', 'h1_arterial_po2_max', 'd1_albumin_min', 'h1_arterial_pco2_max', 'h1_hco3_max', 'h1_potassium_min', 'h1_hematocrit_max', 'h1_bilirubin_max']

para aqui

Model

# percentage of death , hopefully it s a bit unbalanced
train['hospital_death'].sum()/train['hospital_death'].count()

0.08630183289173836

Hyper parameter tuning

# You want Bayesian Optimization?

boll_BayesianOptimization = False
#boll_BayesianOptimization = True

def LGB_Beyes(subsample_freq,
                    learning_rate,
                    feature_fraction,
                    max_depth,
                    lambda_l1,
                    lambda_l2,
                    scale_pos_weight):
    params={}
    params['subsample_freq']=subsample_freq
    params['learning_rate']=learning_rate
    params['feature_fraction']=feature_fraction
    params['lambda_l1']=lambda_l1
    params['lambda_l2']=lambda_l2
    params['max_depth']=max_depth
    params['scale_pos_weight']=scale_pos_weight
    
    lgb_model= Lgb_Model(train, test, features, categoricals=categoricals_features,ps=params)
    print('auc: ',lgb_model.score)
    return lgb_model.score

bounds_LGB = {
    'subsample_freq': (1, 10),
    'learning_rate': (0.005, 0.02),
    'feature_fraction': (0.5, 1),
    'lambda_l1': (0, 5),
    'lambda_l2': (0, 5),
    'max_depth': (13, 17),
    'scale_pos_weight': (1, 10),
}

# ACTIVATE it if you want to search for better parameter
if boll_BayesianOptimization: 
    LGB_BO = BayesianOptimization(LGB_Beyes, bounds_LGB, random_state=1029)
    import warnings
    init_points = 16
    n_iter = 16
    with warnings.catch_warnings():
        warnings.filterwarnings('ignore')    
        LGB_BO.maximize(init_points=init_points, n_iter=n_iter, acq='ucb', xi=0.0, alpha=1e-6)

LGB_BO.max['params']

# params = {'feature_fraction': 0.9,
#  'lambda_l1': 1,
#  'lambda_l2': 1,
#  'learning_rate': 0.1,
#  'max_depth': 13,
#  'subsample_freq': 1,
#  'scale_pos_weight':1}

# Best Hyperparams from Bayesian Optimization in notebook lgb-v2
params = {'feature_fraction': 0.524207414205945,
 'lambda_l1': 4.171808735757517,
 'lambda_l2': 4.6435328298317256,
 'learning_rate': 0.007897539397989824,
 'max_depth': 16.62053004755999,
 'scale_pos_weight': 1.2199266532301127,
 'subsample_freq': 1.0276518730971627}

if boll_BayesianOptimization: # ACTIVATE it if you want to search/use for better parameter
    lgb_model = Lgb_Model(train,test, features, categoricals=categoricals_features, ps= LGB_BO.max['params'])
else :
    lgb_model = Lgb_Model(train,test, features, categoricals=categoricals_features, ps=params)

Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.898677	valid_1's auc: 0.886274
[200]	training's auc: 0.907174	valid_1's auc: 0.890977
[300]	training's auc: 0.914479	valid_1's auc: 0.894686
[400]	training's auc: 0.920499	valid_1's auc: 0.898201
[500]	training's auc: 0.925519	valid_1's auc: 0.900704
[600]	training's auc: 0.929659	valid_1's auc: 0.902461
[700]	training's auc: 0.933339	valid_1's auc: 0.903832
[800]	training's auc: 0.936776	valid_1's auc: 0.90499
[900]	training's auc: 0.939956	valid_1's auc: 0.905722
[1000]	training's auc: 0.942845	valid_1's auc: 0.906288
[1100]	training's auc: 0.945601	valid_1's auc: 0.906736
[1200]	training's auc: 0.948397	valid_1's auc: 0.907179
[1300]	training's auc: 0.95104	valid_1's auc: 0.907603
[1400]	training's auc: 0.953469	valid_1's auc: 0.907968
[1500]	training's auc: 0.955749	valid_1's auc: 0.908317
[1600]	training's auc: 0.95782	valid_1's auc: 0.908587
[1700]	training's auc: 0.959813	valid_1's auc: 0.908741
[1800]	training's auc: 0.961848	valid_1's auc: 0.908861
[1900]	training's auc: 0.96372	valid_1's auc: 0.909003
[2000]	training's auc: 0.965516	valid_1's auc: 0.909166
[2100]	training's auc: 0.967129	valid_1's auc: 0.909313
[2200]	training's auc: 0.968729	valid_1's auc: 0.909365
[2300]	training's auc: 0.970169	valid_1's auc: 0.90946
[2400]	training's auc: 0.971649	valid_1's auc: 0.909497
[2500]	training's auc: 0.973017	valid_1's auc: 0.909571
[2600]	training's auc: 0.974316	valid_1's auc: 0.909645
[2700]	training's auc: 0.975524	valid_1's auc: 0.909721
[2800]	training's auc: 0.976702	valid_1's auc: 0.909757
[2900]	training's auc: 0.977867	valid_1's auc: 0.909737
Early stopping, best iteration is:
[2840]	training's auc: 0.977173	valid_1's auc: 0.909779
Partial score of fold 0 is: 0.9097794737342016
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.899068	valid_1's auc: 0.884299
[200]	training's auc: 0.90755	valid_1's auc: 0.889147
[300]	training's auc: 0.914959	valid_1's auc: 0.893216
[400]	training's auc: 0.920768	valid_1's auc: 0.89633
[500]	training's auc: 0.925772	valid_1's auc: 0.898884
[600]	training's auc: 0.929906	valid_1's auc: 0.900712
[700]	training's auc: 0.933483	valid_1's auc: 0.902192
[800]	training's auc: 0.936827	valid_1's auc: 0.903358
[900]	training's auc: 0.939992	valid_1's auc: 0.904171
[1000]	training's auc: 0.943004	valid_1's auc: 0.904919
[1100]	training's auc: 0.945924	valid_1's auc: 0.905377
[1200]	training's auc: 0.948709	valid_1's auc: 0.905763
[1300]	training's auc: 0.951171	valid_1's auc: 0.906128
[1400]	training's auc: 0.953558	valid_1's auc: 0.906477
[1500]	training's auc: 0.955828	valid_1's auc: 0.906676
[1600]	training's auc: 0.958041	valid_1's auc: 0.906969
[1700]	training's auc: 0.960017	valid_1's auc: 0.907271
[1800]	training's auc: 0.96194	valid_1's auc: 0.907441
[1900]	training's auc: 0.963908	valid_1's auc: 0.907513
[2000]	training's auc: 0.965671	valid_1's auc: 0.907666
Early stopping, best iteration is:
[1971]	training's auc: 0.965172	valid_1's auc: 0.907705
Partial score of fold 1 is: 0.9077046995448358
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.900237	valid_1's auc: 0.882619
[200]	training's auc: 0.90813	valid_1's auc: 0.886549
[300]	training's auc: 0.915186	valid_1's auc: 0.890553
[400]	training's auc: 0.921166	valid_1's auc: 0.894062
[500]	training's auc: 0.926059	valid_1's auc: 0.896547
[600]	training's auc: 0.930169	valid_1's auc: 0.898198
[700]	training's auc: 0.933927	valid_1's auc: 0.899476
[800]	training's auc: 0.937257	valid_1's auc: 0.90042
[900]	training's auc: 0.940414	valid_1's auc: 0.901054
[1000]	training's auc: 0.943383	valid_1's auc: 0.901649
[1100]	training's auc: 0.946198	valid_1's auc: 0.902056
[1200]	training's auc: 0.949003	valid_1's auc: 0.902441
[1300]	training's auc: 0.951499	valid_1's auc: 0.902683
[1400]	training's auc: 0.953793	valid_1's auc: 0.902796
[1500]	training's auc: 0.956059	valid_1's auc: 0.903021
[1600]	training's auc: 0.958204	valid_1's auc: 0.90335
[1700]	training's auc: 0.960352	valid_1's auc: 0.903502
[1800]	training's auc: 0.962306	valid_1's auc: 0.903579
[1900]	training's auc: 0.964176	valid_1's auc: 0.903618
[2000]	training's auc: 0.965861	valid_1's auc: 0.90371
[2100]	training's auc: 0.967512	valid_1's auc: 0.903763
[2200]	training's auc: 0.96908	valid_1's auc: 0.903835
[2300]	training's auc: 0.970616	valid_1's auc: 0.903935
[2400]	training's auc: 0.972014	valid_1's auc: 0.904008
[2500]	training's auc: 0.973306	valid_1's auc: 0.90408
[2600]	training's auc: 0.974593	valid_1's auc: 0.904099
[2700]	training's auc: 0.975829	valid_1's auc: 0.904206
[2800]	training's auc: 0.977054	valid_1's auc: 0.904207
Early stopping, best iteration is:
[2733]	training's auc: 0.97621	valid_1's auc: 0.904243
Partial score of fold 2 is: 0.9042428728871951
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.899447	valid_1's auc: 0.888485
[200]	training's auc: 0.907282	valid_1's auc: 0.892743
[300]	training's auc: 0.914599	valid_1's auc: 0.896122
[400]	training's auc: 0.92077	valid_1's auc: 0.898992
[500]	training's auc: 0.925777	valid_1's auc: 0.901147
[600]	training's auc: 0.929985	valid_1's auc: 0.902574
[700]	training's auc: 0.933669	valid_1's auc: 0.903752
[800]	training's auc: 0.937096	valid_1's auc: 0.904701
[900]	training's auc: 0.940262	valid_1's auc: 0.905335
[1000]	training's auc: 0.943291	valid_1's auc: 0.905778
[1100]	training's auc: 0.946156	valid_1's auc: 0.90625
[1200]	training's auc: 0.948924	valid_1's auc: 0.906572
[1300]	training's auc: 0.951548	valid_1's auc: 0.906702
[1400]	training's auc: 0.95401	valid_1's auc: 0.906914
[1500]	training's auc: 0.956363	valid_1's auc: 0.907025
[1600]	training's auc: 0.95851	valid_1's auc: 0.907127
[1700]	training's auc: 0.960485	valid_1's auc: 0.90732
[1800]	training's auc: 0.962346	valid_1's auc: 0.907408
[1900]	training's auc: 0.9642	valid_1's auc: 0.907486
[2000]	training's auc: 0.965962	valid_1's auc: 0.907634
[2100]	training's auc: 0.967628	valid_1's auc: 0.907648
[2200]	training's auc: 0.969253	valid_1's auc: 0.907713
[2300]	training's auc: 0.970743	valid_1's auc: 0.907753
[2400]	training's auc: 0.972202	valid_1's auc: 0.907732
Early stopping, best iteration is:
[2311]	training's auc: 0.970898	valid_1's auc: 0.907758
Partial score of fold 3 is: 0.907757617869649
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.899023	valid_1's auc: 0.885131
[200]	training's auc: 0.907248	valid_1's auc: 0.889942
[300]	training's auc: 0.914309	valid_1's auc: 0.893918
[400]	training's auc: 0.920175	valid_1's auc: 0.897536
[500]	training's auc: 0.925375	valid_1's auc: 0.900507
[600]	training's auc: 0.929591	valid_1's auc: 0.902426
[700]	training's auc: 0.933307	valid_1's auc: 0.903708
[800]	training's auc: 0.936729	valid_1's auc: 0.904791
[900]	training's auc: 0.939917	valid_1's auc: 0.905627
[1000]	training's auc: 0.942933	valid_1's auc: 0.906228
[1100]	training's auc: 0.945727	valid_1's auc: 0.906642
[1200]	training's auc: 0.948485	valid_1's auc: 0.907061
[1300]	training's auc: 0.951069	valid_1's auc: 0.907496
[1400]	training's auc: 0.953521	valid_1's auc: 0.907781
[1500]	training's auc: 0.955829	valid_1's auc: 0.907985
[1600]	training's auc: 0.958028	valid_1's auc: 0.90821
[1700]	training's auc: 0.960065	valid_1's auc: 0.90835
[1800]	training's auc: 0.962064	valid_1's auc: 0.908502
[1900]	training's auc: 0.963915	valid_1's auc: 0.908636
[2000]	training's auc: 0.965694	valid_1's auc: 0.908752
[2100]	training's auc: 0.967341	valid_1's auc: 0.908908
[2200]	training's auc: 0.968943	valid_1's auc: 0.909047
[2300]	training's auc: 0.970445	valid_1's auc: 0.90909
[2400]	training's auc: 0.971929	valid_1's auc: 0.909103
Early stopping, best iteration is:
[2337]	training's auc: 0.970987	valid_1's auc: 0.909153
Partial score of fold 4 is: 0.9091533850038696
Our oof AUC score is:  0.9076595694573828

# Train lgb_model = Lgb_Model(train,test, features, categoricals=categoricals_features, ps=params)

Feature Importance from the lightgbm model (gain)

imp_df = pd.DataFrame()
imp_df['feature'] = features
imp_df['gain']  = lgb_model.model.feature_importance(importance_type='gain')
imp_df['split'] = lgb_model.model.feature_importance(importance_type='split')

def plot_importances(importances_):
    mean_gain = importances_[['gain', 'feature']].groupby('feature').mean()
    importances_['mean_gain'] = importances_['feature'].map(mean_gain['gain'])
    plt.figure(figsize=(18, 44))
    data_imp = importances_.sort_values('mean_gain', ascending=False)
    sns.barplot(x='gain', y='feature', data=data_imp[:300])
    plt.tight_layout()
    plt.savefig('importances-lgb-v2.png')
    plt.show()

plot_importances(imp_df)

Feature Importance by permutation importance algo

import shap
explainer   =  shap.TreeExplainer(lgb_model.model)
shap_values = explainer.shap_values(train[features].iloc[:1000,:])
shap.summary_plot(shap_values, train[features].iloc[:1000,:])

Some univariate plot of the best feature

import warnings
warnings.filterwarnings("ignore")
warnings.simplefilter(action='ignore', category=UserWarning)
i=0
for index, row in imp_df.sort_values(by=['gain'],ascending=False).iterrows():  
    column=row['feature']
    if i< 50:
            print(column,i,"gain :",row['gain'])
            df1      = train.loc[train['hospital_death']==0]
            df2      = train.loc[train['hospital_death']==1]

            fig = plt.figure(figsize=(20,4))
            sns.distplot(df1[column].dropna(),  color='red', label='hospital_death 0', kde=True); 
            sns.distplot(df2[column].dropna(),  color='blue', label='hospital_death 1', kde=True); 
            fig=plt.legend(loc='best')
            plt.xlabel(column, fontsize=12);
            plt.show()
            i=i+1

apache_4a_hospital_death_prob 0 gain : 351252.365885973

hospital_id 1 gain : 150560.84422779083

apache_4a_icu_death_prob 2 gain : 146275.1173324585

d1_lactate_min 3 gain : 55353.86215758324

d1_spo2_min 4 gain : 31732.761485099792

ventilated_apache 5 gain : 28691.33399581909

d1_sysbp_min 6 gain : 24419.43428516388

d1_heartrate_min 7 gain : 21757.668104171753

age 8 gain : 21481.372059106827

d1_bun_min 9 gain : 19529.879061460495

apache_3j_diagnosis 10 gain : 18576.375860452652

gcs_motor_apache 11 gain : 16573.00981283188

d1_temp_max 12 gain : 16168.361726999283

d1_sysbp_noninvasive_min 13 gain : 16028.458354949951

d1_lactate_max 14 gain : 14038.338047266006

urineoutput_apache 15 gain : 13783.985323429108

gcs_eyes_apache 16 gain : 13053.265293121338

d1_platelets_min 17 gain : 12857.688428878784

d1_arterial_ph_min 18 gain : 12081.881103992462

d1_resprate_min 19 gain : 11519.11014342308

d1_bun_max 20 gain : 11254.286282539368

bmi 21 gain : 10985.150521993637

d1_temp_min 22 gain : 10853.892260551453

apache_3j_bodysystem 23 gain : 10575.751663684845

d1_resprate_max 24 gain : 10320.771817922592

d1_heartrate_max 25 gain : 9808.910019397736

d1_glucose_min 26 gain : 9175.222955703735

d1_wbc_min 27 gain : 9171.61188173294

d1_pao2fio2ratio_max 28 gain : 8934.969403982162

apache_2_diagnosis 29 gain : 8857.88154411316

wbc_apache 30 gain : 8504.452875375748

d1_sodium_max 31 gain : 8418.125189781189

d1_arterial_ph_max 32 gain : 7989.068685293198

d1_hco3_min 33 gain : 7691.476171255112

glucose_apache 34 gain : 7641.672451019287

pre_icu_los_days 35 gain : 7494.74694108963

h1_resprate_min 36 gain : 7485.923817396164

creatinine_apache 37 gain : 7413.3983066082

temp_apache 38 gain : 7281.159093618393

weight 39 gain : 6915.9319133758545

d1_platelets_max 40 gain : 6849.855688333511

d1_pao2fio2ratio_min 41 gain : 6763.60645365715

d1_sysbp_noninvasive_max 42 gain : 6714.262719631195

apache_2_bodysystem 43 gain : 6507.397555351257

d1_mbp_min 44 gain : 6398.961150884628

d1_hco3_max 45 gain : 6097.269358158112

d1_inr_max 46 gain : 5984.607861280441

resprate_apache 47 gain : 5856.124365568161

d1_hemaglobin_max 48 gain : 5758.485659599304

d1_mbp_noninvasive_min 49 gain : 5615.044913053513

Feature Selection

# Find the features with zero importance

imp_df_sorted = imp_df.sort_values('gain', ascending = False)

zero_features = list(imp_df_sorted[imp_df_sorted['gain'] == 0.0]['feature'])

print('There are %d features with 0.0 importance' % len(zero_features))
imp_df_sorted.tail()

# Drop features with zero importance
print('\nLength train features: {}'.format(len(features)))
for feat_to_remove in zero_features:
    print('Removing....{}'.format(feat_to_remove))
    features.remove(feat_to_remove)

print('\nNew length train features: {}'.format(len(features)))    

There are 4 features with 0.0 importance
Length train features: 181
Removing....gcs_unable_apache
Removing....lymphoma
Removing....readmission_status
Removing....aids
New length train features: 177

New Model with Feature Selection and Parametes Tuning

# Hyper parameter tuning

boll_BayesianOptimization = True

# ACTIVATE it if you want to search for better parameter
if boll_BayesianOptimization: 
    LGB_BO_v2 = BayesianOptimization(LGB_Beyes, bounds_LGB, random_state=1029)
    import warnings
    init_points = 16
    n_iter = 16
    with warnings.catch_warnings():
        warnings.filterwarnings('ignore')    
        LGB_BO_v2.maximize(init_points=init_points, n_iter=n_iter, acq='ucb', xi=0.0, alpha=1e-6)

|   iter    |  target   | featur... | lambda_l1 | lambda_l2 | learni... | max_depth | scale_... | subsam... |
-------------------------------------------------------------------------------------------------------------
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.915745	valid_1's auc: 0.894274
[200]	training's auc: 0.929449	valid_1's auc: 0.900922
[300]	training's auc: 0.939118	valid_1's auc: 0.903915
[400]	training's auc: 0.947524	valid_1's auc: 0.905663
[500]	training's auc: 0.954915	valid_1's auc: 0.906699
[600]	training's auc: 0.96086	valid_1's auc: 0.907443
[700]	training's auc: 0.966165	valid_1's auc: 0.907972
[800]	training's auc: 0.970688	valid_1's auc: 0.908426
[900]	training's auc: 0.974693	valid_1's auc: 0.908501
[1000]	training's auc: 0.978114	valid_1's auc: 0.908663
[1100]	training's auc: 0.981027	valid_1's auc: 0.908922
[1200]	training's auc: 0.983655	valid_1's auc: 0.9089
[1300]	training's auc: 0.985891	valid_1's auc: 0.908956
Early stopping, best iteration is:
[1234]	training's auc: 0.984412	valid_1's auc: 0.909113
Partial score of fold 0 is: 0.9091128593332802
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.916071	valid_1's auc: 0.892631
[200]	training's auc: 0.929588	valid_1's auc: 0.899033
[300]	training's auc: 0.93961	valid_1's auc: 0.90263
[400]	training's auc: 0.947791	valid_1's auc: 0.904558
[500]	training's auc: 0.955089	valid_1's auc: 0.905599
[600]	training's auc: 0.961304	valid_1's auc: 0.906383
[700]	training's auc: 0.966563	valid_1's auc: 0.907053
[800]	training's auc: 0.971112	valid_1's auc: 0.907244
[900]	training's auc: 0.975173	valid_1's auc: 0.90744
[1000]	training's auc: 0.978587	valid_1's auc: 0.907438
[1100]	training's auc: 0.981576	valid_1's auc: 0.907536
[1200]	training's auc: 0.984184	valid_1's auc: 0.907559
[1300]	training's auc: 0.986435	valid_1's auc: 0.907436
Early stopping, best iteration is:
[1234]	training's auc: 0.984925	valid_1's auc: 0.907672
Partial score of fold 1 is: 0.9076717947403574
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.916377	valid_1's auc: 0.889303
[200]	training's auc: 0.930285	valid_1's auc: 0.89644
[300]	training's auc: 0.93999	valid_1's auc: 0.899653
[400]	training's auc: 0.948346	valid_1's auc: 0.901015
[500]	training's auc: 0.955594	valid_1's auc: 0.902114
[600]	training's auc: 0.961435	valid_1's auc: 0.90237
[700]	training's auc: 0.966841	valid_1's auc: 0.902828
[800]	training's auc: 0.971446	valid_1's auc: 0.903271
[900]	training's auc: 0.975307	valid_1's auc: 0.903312
[1000]	training's auc: 0.978859	valid_1's auc: 0.903358
[1100]	training's auc: 0.981783	valid_1's auc: 0.903207
Early stopping, best iteration is:
[1005]	training's auc: 0.978992	valid_1's auc: 0.903387
Partial score of fold 2 is: 0.9033874233540437
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.916015	valid_1's auc: 0.893653
[200]	training's auc: 0.929973	valid_1's auc: 0.900102
[300]	training's auc: 0.939917	valid_1's auc: 0.902558
[400]	training's auc: 0.948393	valid_1's auc: 0.904157
[500]	training's auc: 0.955253	valid_1's auc: 0.905226
[600]	training's auc: 0.961531	valid_1's auc: 0.905596
[700]	training's auc: 0.966552	valid_1's auc: 0.905928
[800]	training's auc: 0.971296	valid_1's auc: 0.906072
[900]	training's auc: 0.975241	valid_1's auc: 0.906272
[1000]	training's auc: 0.978632	valid_1's auc: 0.906601
[1100]	training's auc: 0.981568	valid_1's auc: 0.90669
Early stopping, best iteration is:
[1058]	training's auc: 0.980272	valid_1's auc: 0.906827
Partial score of fold 3 is: 0.9068272949162964
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.916085	valid_1's auc: 0.893446
[200]	training's auc: 0.930002	valid_1's auc: 0.901141
[300]	training's auc: 0.940095	valid_1's auc: 0.904388
[400]	training's auc: 0.948165	valid_1's auc: 0.905871
[500]	training's auc: 0.955275	valid_1's auc: 0.906715
[600]	training's auc: 0.961163	valid_1's auc: 0.907361
[700]	training's auc: 0.966659	valid_1's auc: 0.907702
[800]	training's auc: 0.971363	valid_1's auc: 0.907902
Early stopping, best iteration is:
[781]	training's auc: 0.970487	valid_1's auc: 0.907923
Partial score of fold 4 is: 0.9079234333014305
Our oof AUC score is:  0.9068143111709677
auc:  0.9068143111709677
|  1        |  0.9068   |  0.5205   |  2.049    |  2.944    |  0.0185   |  13.48    |  2.532    |  9.462    |
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.912766	valid_1's auc: 0.891295
[200]	training's auc: 0.925916	valid_1's auc: 0.897542
[300]	training's auc: 0.934977	valid_1's auc: 0.90108
[400]	training's auc: 0.942086	valid_1's auc: 0.903123
[500]	training's auc: 0.948382	valid_1's auc: 0.904424
[600]	training's auc: 0.954018	valid_1's auc: 0.905343
[700]	training's auc: 0.958997	valid_1's auc: 0.90604
[800]	training's auc: 0.9634	valid_1's auc: 0.906576
[900]	training's auc: 0.967144	valid_1's auc: 0.906882
[1000]	training's auc: 0.970695	valid_1's auc: 0.907051
[1100]	training's auc: 0.973587	valid_1's auc: 0.907422
[1200]	training's auc: 0.976449	valid_1's auc: 0.907647
[1300]	training's auc: 0.978946	valid_1's auc: 0.90785
[1400]	training's auc: 0.981099	valid_1's auc: 0.907935
[1500]	training's auc: 0.983129	valid_1's auc: 0.908193
[1600]	training's auc: 0.984853	valid_1's auc: 0.908301
[1700]	training's auc: 0.986401	valid_1's auc: 0.908385
[1800]	training's auc: 0.987811	valid_1's auc: 0.908406
[1900]	training's auc: 0.989151	valid_1's auc: 0.908404
Early stopping, best iteration is:
[1842]	training's auc: 0.988371	valid_1's auc: 0.908541
Partial score of fold 0 is: 0.9085413786396934
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.912636	valid_1's auc: 0.886772
[200]	training's auc: 0.925857	valid_1's auc: 0.894136
[300]	training's auc: 0.935148	valid_1's auc: 0.898635
[400]	training's auc: 0.942356	valid_1's auc: 0.900809
[500]	training's auc: 0.948772	valid_1's auc: 0.902057
[600]	training's auc: 0.954597	valid_1's auc: 0.903011
[700]	training's auc: 0.959565	valid_1's auc: 0.903947
[800]	training's auc: 0.964031	valid_1's auc: 0.904266
[900]	training's auc: 0.967856	valid_1's auc: 0.904531
[1000]	training's auc: 0.971065	valid_1's auc: 0.90483
[1100]	training's auc: 0.974138	valid_1's auc: 0.904968
[1200]	training's auc: 0.976917	valid_1's auc: 0.90534
[1300]	training's auc: 0.979316	valid_1's auc: 0.905216
Early stopping, best iteration is:
[1218]	training's auc: 0.977354	valid_1's auc: 0.905367
Partial score of fold 1 is: 0.9053673653692199
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.913166	valid_1's auc: 0.884398
[200]	training's auc: 0.926498	valid_1's auc: 0.89125
[300]	training's auc: 0.935552	valid_1's auc: 0.895176
[400]	training's auc: 0.94298	valid_1's auc: 0.896817
[500]	training's auc: 0.949304	valid_1's auc: 0.898398
[600]	training's auc: 0.95491	valid_1's auc: 0.899292
[700]	training's auc: 0.959992	valid_1's auc: 0.899944
[800]	training's auc: 0.964452	valid_1's auc: 0.900362
[900]	training's auc: 0.96801	valid_1's auc: 0.900693
[1000]	training's auc: 0.97156	valid_1's auc: 0.900942
[1100]	training's auc: 0.974507	valid_1's auc: 0.901038
Early stopping, best iteration is:
[1066]	training's auc: 0.973564	valid_1's auc: 0.901097
Partial score of fold 2 is: 0.9010969775825185
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.912766	valid_1's auc: 0.890525
[200]	training's auc: 0.926168	valid_1's auc: 0.896432
[300]	training's auc: 0.935238	valid_1's auc: 0.899943
[400]	training's auc: 0.94271	valid_1's auc: 0.901779
[500]	training's auc: 0.948933	valid_1's auc: 0.902938
[600]	training's auc: 0.954891	valid_1's auc: 0.903812
[700]	training's auc: 0.959745	valid_1's auc: 0.904381
[800]	training's auc: 0.964125	valid_1's auc: 0.904615
[900]	training's auc: 0.967925	valid_1's auc: 0.904865
[1000]	training's auc: 0.971317	valid_1's auc: 0.905003
[1100]	training's auc: 0.974336	valid_1's auc: 0.905219
[1200]	training's auc: 0.976984	valid_1's auc: 0.905265
[1300]	training's auc: 0.979433	valid_1's auc: 0.905383
Early stopping, best iteration is:
[1284]	training's auc: 0.979044	valid_1's auc: 0.90546
Partial score of fold 3 is: 0.905459647425656
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.91239	valid_1's auc: 0.88693
[200]	training's auc: 0.925685	valid_1's auc: 0.895365
[300]	training's auc: 0.934726	valid_1's auc: 0.899883
[400]	training's auc: 0.942211	valid_1's auc: 0.90197
[500]	training's auc: 0.948543	valid_1's auc: 0.903245
[600]	training's auc: 0.953941	valid_1's auc: 0.904119
[700]	training's auc: 0.959045	valid_1's auc: 0.905019
[800]	training's auc: 0.96351	valid_1's auc: 0.905626
[900]	training's auc: 0.967563	valid_1's auc: 0.906019
[1000]	training's auc: 0.970928	valid_1's auc: 0.906479
[1100]	training's auc: 0.973787	valid_1's auc: 0.906618
[1200]	training's auc: 0.976549	valid_1's auc: 0.906762
[1300]	training's auc: 0.979084	valid_1's auc: 0.906981
[1400]	training's auc: 0.981339	valid_1's auc: 0.907093
[1500]	training's auc: 0.983273	valid_1's auc: 0.907153
[1600]	training's auc: 0.984906	valid_1's auc: 0.907337
Early stopping, best iteration is:
[1561]	training's auc: 0.984235	valid_1's auc: 0.907374
Partial score of fold 4 is: 0.9073740071287444
Our oof AUC score is:  0.9054978689616339
auc:  0.9054978689616339
|  2        |  0.9055   |  0.7977   |  1.678    |  2.208    |  0.01244  |  13.35    |  3.467    |  6.94     |
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.908657	valid_1's auc: 0.888882
[200]	training's auc: 0.917431	valid_1's auc: 0.893119
[300]	training's auc: 0.924325	valid_1's auc: 0.896354
[400]	training's auc: 0.930229	valid_1's auc: 0.898766
[500]	training's auc: 0.935376	valid_1's auc: 0.900695
[600]	training's auc: 0.939868	valid_1's auc: 0.902106
[700]	training's auc: 0.943861	valid_1's auc: 0.903082
[800]	training's auc: 0.947472	valid_1's auc: 0.904062
[900]	training's auc: 0.950905	valid_1's auc: 0.904573
[1000]	training's auc: 0.954048	valid_1's auc: 0.904843
[1100]	training's auc: 0.957003	valid_1's auc: 0.905257
[1200]	training's auc: 0.959776	valid_1's auc: 0.905614
[1300]	training's auc: 0.962316	valid_1's auc: 0.906016
[1400]	training's auc: 0.964673	valid_1's auc: 0.906287
[1500]	training's auc: 0.966939	valid_1's auc: 0.906504
[1600]	training's auc: 0.969012	valid_1's auc: 0.906666
[1700]	training's auc: 0.970843	valid_1's auc: 0.906698
Early stopping, best iteration is:
[1643]	training's auc: 0.969813	valid_1's auc: 0.90677
Partial score of fold 0 is: 0.9067698714111903
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.909158	valid_1's auc: 0.886808
[200]	training's auc: 0.917844	valid_1's auc: 0.891624
[300]	training's auc: 0.924763	valid_1's auc: 0.895132
[400]	training's auc: 0.930819	valid_1's auc: 0.898123
[500]	training's auc: 0.935978	valid_1's auc: 0.900254
[600]	training's auc: 0.940409	valid_1's auc: 0.901693
[700]	training's auc: 0.944429	valid_1's auc: 0.902818
[800]	training's auc: 0.94802	valid_1's auc: 0.903661
[900]	training's auc: 0.95149	valid_1's auc: 0.904273
[1000]	training's auc: 0.954591	valid_1's auc: 0.904755
[1100]	training's auc: 0.957557	valid_1's auc: 0.905084
[1200]	training's auc: 0.960332	valid_1's auc: 0.90537
[1300]	training's auc: 0.962835	valid_1's auc: 0.905678
[1400]	training's auc: 0.965158	valid_1's auc: 0.905914
[1500]	training's auc: 0.967432	valid_1's auc: 0.906025
[1600]	training's auc: 0.969472	valid_1's auc: 0.906148
[1700]	training's auc: 0.971394	valid_1's auc: 0.906264
[1800]	training's auc: 0.973107	valid_1's auc: 0.90629
[1900]	training's auc: 0.974779	valid_1's auc: 0.906392
[2000]	training's auc: 0.976335	valid_1's auc: 0.906434
[2100]	training's auc: 0.977811	valid_1's auc: 0.90648
[2200]	training's auc: 0.979225	valid_1's auc: 0.906514
[2300]	training's auc: 0.980492	valid_1's auc: 0.906463
Early stopping, best iteration is:
[2211]	training's auc: 0.979362	valid_1's auc: 0.906538
Partial score of fold 1 is: 0.906537954730829
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.909537	valid_1's auc: 0.88415
[200]	training's auc: 0.91813	valid_1's auc: 0.887808
[300]	training's auc: 0.925029	valid_1's auc: 0.891544
[400]	training's auc: 0.93117	valid_1's auc: 0.894647
[500]	training's auc: 0.93617	valid_1's auc: 0.89687
[600]	training's auc: 0.940731	valid_1's auc: 0.898245
[700]	training's auc: 0.944703	valid_1's auc: 0.899361
[800]	training's auc: 0.948314	valid_1's auc: 0.900177
[900]	training's auc: 0.951672	valid_1's auc: 0.900846
[1000]	training's auc: 0.954769	valid_1's auc: 0.901425
[1100]	training's auc: 0.957751	valid_1's auc: 0.901735
[1200]	training's auc: 0.960513	valid_1's auc: 0.902019
[1300]	training's auc: 0.963006	valid_1's auc: 0.902309
[1400]	training's auc: 0.965325	valid_1's auc: 0.902574
[1500]	training's auc: 0.967445	valid_1's auc: 0.902626
[1600]	training's auc: 0.969591	valid_1's auc: 0.902791
[1700]	training's auc: 0.971465	valid_1's auc: 0.902807
Early stopping, best iteration is:
[1672]	training's auc: 0.970959	valid_1's auc: 0.902838
Partial score of fold 2 is: 0.9028375399719878
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.909349	valid_1's auc: 0.888174
[200]	training's auc: 0.917738	valid_1's auc: 0.891826
[300]	training's auc: 0.924703	valid_1's auc: 0.894905
[400]	training's auc: 0.930723	valid_1's auc: 0.897458
[500]	training's auc: 0.935848	valid_1's auc: 0.899443
[600]	training's auc: 0.940387	valid_1's auc: 0.901036
[700]	training's auc: 0.944397	valid_1's auc: 0.902095
[800]	training's auc: 0.948068	valid_1's auc: 0.902939
[900]	training's auc: 0.95163	valid_1's auc: 0.903561
[1000]	training's auc: 0.954812	valid_1's auc: 0.904064
[1100]	training's auc: 0.957777	valid_1's auc: 0.904471
[1200]	training's auc: 0.96058	valid_1's auc: 0.904788
[1300]	training's auc: 0.963081	valid_1's auc: 0.90507
[1400]	training's auc: 0.96541	valid_1's auc: 0.905315
[1500]	training's auc: 0.967671	valid_1's auc: 0.905373
[1600]	training's auc: 0.969727	valid_1's auc: 0.905465
[1700]	training's auc: 0.971578	valid_1's auc: 0.905607
[1800]	training's auc: 0.97333	valid_1's auc: 0.90566
Early stopping, best iteration is:
[1791]	training's auc: 0.973199	valid_1's auc: 0.905669
Partial score of fold 3 is: 0.9056689239151424
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.90925	valid_1's auc: 0.884657
[200]	training's auc: 0.917603	valid_1's auc: 0.889814
[300]	training's auc: 0.924462	valid_1's auc: 0.89455
[400]	training's auc: 0.930475	valid_1's auc: 0.898051
[500]	training's auc: 0.935592	valid_1's auc: 0.900703
[600]	training's auc: 0.940012	valid_1's auc: 0.902313
[700]	training's auc: 0.944098	valid_1's auc: 0.90344
[800]	training's auc: 0.947787	valid_1's auc: 0.90445
[900]	training's auc: 0.951201	valid_1's auc: 0.905061
[1000]	training's auc: 0.954377	valid_1's auc: 0.905632
[1100]	training's auc: 0.957365	valid_1's auc: 0.906
[1200]	training's auc: 0.960188	valid_1's auc: 0.906329
[1300]	training's auc: 0.962697	valid_1's auc: 0.906475
[1400]	training's auc: 0.965005	valid_1's auc: 0.906638
[1500]	training's auc: 0.967156	valid_1's auc: 0.906691
[1600]	training's auc: 0.969191	valid_1's auc: 0.906778
[1700]	training's auc: 0.971138	valid_1's auc: 0.906864
[1800]	training's auc: 0.972932	valid_1's auc: 0.906889
Early stopping, best iteration is:
[1712]	training's auc: 0.971374	valid_1's auc: 0.906905
Partial score of fold 4 is: 0.9069048312525488
Our oof AUC score is:  0.9056476455571794
auc:  0.9056476455571794
|  3        |  0.9056   |  0.5402   |  0.1933   |  3.376    |  0.00781  |  16.76    |  8.841    |  4.225    |
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.918145	valid_1's auc: 0.889415
[200]	training's auc: 0.932454	valid_1's auc: 0.896559
[300]	training's auc: 0.94212	valid_1's auc: 0.900122
[400]	training's auc: 0.94986	valid_1's auc: 0.902318
[500]	training's auc: 0.956364	valid_1's auc: 0.903426
[600]	training's auc: 0.961828	valid_1's auc: 0.904211
[700]	training's auc: 0.966678	valid_1's auc: 0.904611
[800]	training's auc: 0.970928	valid_1's auc: 0.9048
[900]	training's auc: 0.974415	valid_1's auc: 0.905195
[1000]	training's auc: 0.977447	valid_1's auc: 0.905665
[1100]	training's auc: 0.980154	valid_1's auc: 0.905634
Early stopping, best iteration is:
[1065]	training's auc: 0.979239	valid_1's auc: 0.905733
Partial score of fold 0 is: 0.9057328989245821
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.917697	valid_1's auc: 0.885852
[200]	training's auc: 0.932627	valid_1's auc: 0.894352
[300]	training's auc: 0.942454	valid_1's auc: 0.898454
[400]	training's auc: 0.950239	valid_1's auc: 0.900105
[500]	training's auc: 0.956845	valid_1's auc: 0.901149
[600]	training's auc: 0.962488	valid_1's auc: 0.902108
[700]	training's auc: 0.967313	valid_1's auc: 0.902437
[800]	training's auc: 0.971372	valid_1's auc: 0.902628
[900]	training's auc: 0.974875	valid_1's auc: 0.902709
[1000]	training's auc: 0.977881	valid_1's auc: 0.902938
[1100]	training's auc: 0.980573	valid_1's auc: 0.90306
[1200]	training's auc: 0.982916	valid_1's auc: 0.903398
[1300]	training's auc: 0.984958	valid_1's auc: 0.90338
Early stopping, best iteration is:
[1201]	training's auc: 0.982937	valid_1's auc: 0.903409
Partial score of fold 1 is: 0.9034094729652921
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.918536	valid_1's auc: 0.884052
[200]	training's auc: 0.933217	valid_1's auc: 0.892225
[300]	training's auc: 0.942872	valid_1's auc: 0.896093
[400]	training's auc: 0.950757	valid_1's auc: 0.897979
[500]	training's auc: 0.957211	valid_1's auc: 0.899402
[600]	training's auc: 0.962767	valid_1's auc: 0.899876
[700]	training's auc: 0.967398	valid_1's auc: 0.900417
[800]	training's auc: 0.971538	valid_1's auc: 0.90117
[900]	training's auc: 0.974865	valid_1's auc: 0.901497
Early stopping, best iteration is:
[897]	training's auc: 0.97478	valid_1's auc: 0.90154
Partial score of fold 2 is: 0.9015398920812872
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.917932	valid_1's auc: 0.889281
[200]	training's auc: 0.932157	valid_1's auc: 0.895821
[300]	training's auc: 0.94216	valid_1's auc: 0.899024
[400]	training's auc: 0.950111	valid_1's auc: 0.900637
[500]	training's auc: 0.956766	valid_1's auc: 0.901835
[600]	training's auc: 0.962436	valid_1's auc: 0.902367
[700]	training's auc: 0.967192	valid_1's auc: 0.902877
[800]	training's auc: 0.971374	valid_1's auc: 0.903135
[900]	training's auc: 0.974826	valid_1's auc: 0.903373
[1000]	training's auc: 0.97792	valid_1's auc: 0.903621
[1100]	training's auc: 0.980693	valid_1's auc: 0.903729
Early stopping, best iteration is:
[1093]	training's auc: 0.980512	valid_1's auc: 0.903781
Partial score of fold 3 is: 0.9037813268755153
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.917653	valid_1's auc: 0.885118
[200]	training's auc: 0.932133	valid_1's auc: 0.895166
[300]	training's auc: 0.942038	valid_1's auc: 0.899439
[400]	training's auc: 0.949898	valid_1's auc: 0.901547
[500]	training's auc: 0.956485	valid_1's auc: 0.902955
[600]	training's auc: 0.962018	valid_1's auc: 0.903831
[700]	training's auc: 0.966907	valid_1's auc: 0.904567
[800]	training's auc: 0.97098	valid_1's auc: 0.904879
[900]	training's auc: 0.974536	valid_1's auc: 0.905163
[1000]	training's auc: 0.977648	valid_1's auc: 0.905392
Early stopping, best iteration is:
[988]	training's auc: 0.977335	valid_1's auc: 0.905447
Partial score of fold 4 is: 0.9054473215229071
Our oof AUC score is:  0.9038778540284516
auc:  0.9038778540284516
|  4        |  0.9039   |  0.7839   |  4.396    |  2.262    |  0.01558  |  16.18    |  9.494    |  3.734    |
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.909152	valid_1's auc: 0.887679
[200]	training's auc: 0.921142	valid_1's auc: 0.892401
[300]	training's auc: 0.929959	valid_1's auc: 0.896668
[400]	training's auc: 0.936745	valid_1's auc: 0.899254
[500]	training's auc: 0.942353	valid_1's auc: 0.900777
[600]	training's auc: 0.947276	valid_1's auc: 0.901873
[700]	training's auc: 0.951922	valid_1's auc: 0.902769
[800]	training's auc: 0.956011	valid_1's auc: 0.903324
[900]	training's auc: 0.959557	valid_1's auc: 0.903894
[1000]	training's auc: 0.962964	valid_1's auc: 0.904548
[1100]	training's auc: 0.966202	valid_1's auc: 0.904924
[1200]	training's auc: 0.968983	valid_1's auc: 0.905235
[1300]	training's auc: 0.971427	valid_1's auc: 0.905294
[1400]	training's auc: 0.973673	valid_1's auc: 0.905479
[1500]	training's auc: 0.975885	valid_1's auc: 0.905478
[1600]	training's auc: 0.97777	valid_1's auc: 0.905583
Early stopping, best iteration is:
[1586]	training's auc: 0.977501	valid_1's auc: 0.905628
Partial score of fold 0 is: 0.9056281915398845
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.909779	valid_1's auc: 0.883923
[200]	training's auc: 0.921824	valid_1's auc: 0.889637
[300]	training's auc: 0.930907	valid_1's auc: 0.894182
[400]	training's auc: 0.937673	valid_1's auc: 0.896862
[500]	training's auc: 0.943216	valid_1's auc: 0.898752
[600]	training's auc: 0.948135	valid_1's auc: 0.899915
[700]	training's auc: 0.952528	valid_1's auc: 0.900943
[800]	training's auc: 0.956627	valid_1's auc: 0.901305
[900]	training's auc: 0.960295	valid_1's auc: 0.902044
[1000]	training's auc: 0.963669	valid_1's auc: 0.902524
[1100]	training's auc: 0.9668	valid_1's auc: 0.902757
[1200]	training's auc: 0.969514	valid_1's auc: 0.903067
[1300]	training's auc: 0.97207	valid_1's auc: 0.903284
[1400]	training's auc: 0.974311	valid_1's auc: 0.90329
[1500]	training's auc: 0.976499	valid_1's auc: 0.903496
[1600]	training's auc: 0.978454	valid_1's auc: 0.903487
Early stopping, best iteration is:
[1510]	training's auc: 0.976683	valid_1's auc: 0.903586
Partial score of fold 1 is: 0.9035864352299265
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.909962	valid_1's auc: 0.881995
[200]	training's auc: 0.921838	valid_1's auc: 0.887192
[300]	training's auc: 0.930657	valid_1's auc: 0.891603
[400]	training's auc: 0.937406	valid_1's auc: 0.894124
[500]	training's auc: 0.943137	valid_1's auc: 0.89563
[600]	training's auc: 0.9482	valid_1's auc: 0.896814
[700]	training's auc: 0.952652	valid_1's auc: 0.89763
[800]	training's auc: 0.956678	valid_1's auc: 0.898426
[900]	training's auc: 0.960259	valid_1's auc: 0.899019
[1000]	training's auc: 0.963715	valid_1's auc: 0.899417
[1100]	training's auc: 0.966674	valid_1's auc: 0.899607
[1200]	training's auc: 0.969499	valid_1's auc: 0.899756
[1300]	training's auc: 0.972049	valid_1's auc: 0.900014
[1400]	training's auc: 0.974318	valid_1's auc: 0.900295
[1500]	training's auc: 0.976373	valid_1's auc: 0.900449
[1600]	training's auc: 0.97831	valid_1's auc: 0.900564
[1700]	training's auc: 0.98012	valid_1's auc: 0.90071
[1800]	training's auc: 0.981745	valid_1's auc: 0.900683
[1900]	training's auc: 0.983309	valid_1's auc: 0.900733
Early stopping, best iteration is:
[1852]	training's auc: 0.982569	valid_1's auc: 0.900788
Partial score of fold 2 is: 0.9007877553420367
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.909365	valid_1's auc: 0.886831
[200]	training's auc: 0.921161	valid_1's auc: 0.891653
[300]	training's auc: 0.930461	valid_1's auc: 0.895446
[400]	training's auc: 0.937486	valid_1's auc: 0.897959
[500]	training's auc: 0.943189	valid_1's auc: 0.899616
[600]	training's auc: 0.948175	valid_1's auc: 0.900653
[700]	training's auc: 0.952779	valid_1's auc: 0.901336
[800]	training's auc: 0.956983	valid_1's auc: 0.901946
[900]	training's auc: 0.960639	valid_1's auc: 0.902348
[1000]	training's auc: 0.964092	valid_1's auc: 0.902604
[1100]	training's auc: 0.967086	valid_1's auc: 0.902792
[1200]	training's auc: 0.969747	valid_1's auc: 0.903037
[1300]	training's auc: 0.972337	valid_1's auc: 0.903036
[1400]	training's auc: 0.97465	valid_1's auc: 0.903194
[1500]	training's auc: 0.976749	valid_1's auc: 0.903438
[1600]	training's auc: 0.978584	valid_1's auc: 0.903481
[1700]	training's auc: 0.980353	valid_1's auc: 0.903695
[1800]	training's auc: 0.981916	valid_1's auc: 0.90354
Early stopping, best iteration is:
[1702]	training's auc: 0.980391	valid_1's auc: 0.903698
Partial score of fold 3 is: 0.9036978725981876
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.909524	valid_1's auc: 0.884025
[200]	training's auc: 0.921531	valid_1's auc: 0.890292
[300]	training's auc: 0.930467	valid_1's auc: 0.895962
[400]	training's auc: 0.937065	valid_1's auc: 0.899065
[500]	training's auc: 0.942788	valid_1's auc: 0.901071
[600]	training's auc: 0.94781	valid_1's auc: 0.901922
[700]	training's auc: 0.952249	valid_1's auc: 0.902944
[800]	training's auc: 0.956235	valid_1's auc: 0.903625
[900]	training's auc: 0.960056	valid_1's auc: 0.903962
[1000]	training's auc: 0.963273	valid_1's auc: 0.904411
[1100]	training's auc: 0.966322	valid_1's auc: 0.904777
[1200]	training's auc: 0.969143	valid_1's auc: 0.90496
[1300]	training's auc: 0.971763	valid_1's auc: 0.905119
[1400]	training's auc: 0.974055	valid_1's auc: 0.905042
Early stopping, best iteration is:
[1345]	training's auc: 0.972839	valid_1's auc: 0.905201
Partial score of fold 4 is: 0.9052005396106831
Our oof AUC score is:  0.9036425093300728
auc:  0.9036425093300728
|  5        |  0.9036   |  0.9548   |  2.5      |  0.194    |  0.009008 |  13.47    |  3.556    |  5.414    |
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.919486	valid_1's auc: 0.892912
[200]	training's auc: 0.934753	valid_1's auc: 0.899217
[300]	training's auc: 0.945139	valid_1's auc: 0.902067
[400]	training's auc: 0.953371	valid_1's auc: 0.903331
[500]	training's auc: 0.960479	valid_1's auc: 0.904163
[600]	training's auc: 0.966493	valid_1's auc: 0.904857
[700]	training's auc: 0.971523	valid_1's auc: 0.905104
[800]	training's auc: 0.975569	valid_1's auc: 0.905245
[900]	training's auc: 0.978993	valid_1's auc: 0.905294
Early stopping, best iteration is:
[872]	training's auc: 0.978106	valid_1's auc: 0.905473
Partial score of fold 0 is: 0.9054730150449963
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.920447	valid_1's auc: 0.890195
[200]	training's auc: 0.935763	valid_1's auc: 0.897378
[300]	training's auc: 0.945971	valid_1's auc: 0.900168
[400]	training's auc: 0.954325	valid_1's auc: 0.90192
[500]	training's auc: 0.961302	valid_1's auc: 0.903005
[600]	training's auc: 0.967283	valid_1's auc: 0.903265
[700]	training's auc: 0.97204	valid_1's auc: 0.903676
[800]	training's auc: 0.976101	valid_1's auc: 0.903899
[900]	training's auc: 0.979603	valid_1's auc: 0.904015
[1000]	training's auc: 0.982442	valid_1's auc: 0.90426
[1100]	training's auc: 0.984965	valid_1's auc: 0.903842
Early stopping, best iteration is:
[1005]	training's auc: 0.982563	valid_1's auc: 0.904269
Partial score of fold 1 is: 0.9042687293544025
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.920238	valid_1's auc: 0.887972
[200]	training's auc: 0.935694	valid_1's auc: 0.895648
[300]	training's auc: 0.945955	valid_1's auc: 0.898447
[400]	training's auc: 0.954473	valid_1's auc: 0.899597
[500]	training's auc: 0.961537	valid_1's auc: 0.900566
[600]	training's auc: 0.967331	valid_1's auc: 0.900968
[700]	training's auc: 0.972197	valid_1's auc: 0.901256
[800]	training's auc: 0.976349	valid_1's auc: 0.901546
Early stopping, best iteration is:
[760]	training's auc: 0.974676	valid_1's auc: 0.901606
Partial score of fold 2 is: 0.9016060409150325
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.919912	valid_1's auc: 0.892302
[200]	training's auc: 0.935063	valid_1's auc: 0.898482
[300]	training's auc: 0.945573	valid_1's auc: 0.901206
[400]	training's auc: 0.954142	valid_1's auc: 0.902549
[500]	training's auc: 0.960983	valid_1's auc: 0.903288
[600]	training's auc: 0.967023	valid_1's auc: 0.903666
[700]	training's auc: 0.971755	valid_1's auc: 0.903885
[800]	training's auc: 0.975864	valid_1's auc: 0.904136
[900]	training's auc: 0.979489	valid_1's auc: 0.904325
Early stopping, best iteration is:
[852]	training's auc: 0.977743	valid_1's auc: 0.904368
Partial score of fold 3 is: 0.9043681453892625
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.919552	valid_1's auc: 0.890545
[200]	training's auc: 0.934851	valid_1's auc: 0.898636
[300]	training's auc: 0.945287	valid_1's auc: 0.901562
[400]	training's auc: 0.953685	valid_1's auc: 0.903057
[500]	training's auc: 0.960873	valid_1's auc: 0.903826
[600]	training's auc: 0.966705	valid_1's auc: 0.904337
[700]	training's auc: 0.97173	valid_1's auc: 0.904602
[800]	training's auc: 0.975777	valid_1's auc: 0.904772
[900]	training's auc: 0.979268	valid_1's auc: 0.904923
[1000]	training's auc: 0.982226	valid_1's auc: 0.904916
Early stopping, best iteration is:
[923]	training's auc: 0.979997	valid_1's auc: 0.905012
Partial score of fold 4 is: 0.9050117309046606
Our oof AUC score is:  0.9041076624461521
auc:  0.9041076624461521
|  6        |  0.9041   |  0.8786   |  3.534    |  2.64     |  0.01805  |  13.09    |  3.774    |  2.515    |
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.910682	valid_1's auc: 0.88921
[200]	training's auc: 0.920821	valid_1's auc: 0.894234
[300]	training's auc: 0.929203	valid_1's auc: 0.898141
[400]	training's auc: 0.935568	valid_1's auc: 0.900393
[500]	training's auc: 0.941019	valid_1's auc: 0.902047
[600]	training's auc: 0.945654	valid_1's auc: 0.903438
[700]	training's auc: 0.950068	valid_1's auc: 0.904237
[800]	training's auc: 0.954031	valid_1's auc: 0.905053
[900]	training's auc: 0.957758	valid_1's auc: 0.90558
[1000]	training's auc: 0.961044	valid_1's auc: 0.906048
[1100]	training's auc: 0.964058	valid_1's auc: 0.906322
[1200]	training's auc: 0.96684	valid_1's auc: 0.906611
[1300]	training's auc: 0.969325	valid_1's auc: 0.906864
[1400]	training's auc: 0.971609	valid_1's auc: 0.907012
[1500]	training's auc: 0.97377	valid_1's auc: 0.907191
[1600]	training's auc: 0.975716	valid_1's auc: 0.907388
[1700]	training's auc: 0.977448	valid_1's auc: 0.90749
Early stopping, best iteration is:
[1672]	training's auc: 0.976975	valid_1's auc: 0.907554
Partial score of fold 0 is: 0.9075537068223382
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.910998	valid_1's auc: 0.885598
[200]	training's auc: 0.921194	valid_1's auc: 0.891451
[300]	training's auc: 0.929507	valid_1's auc: 0.89585
[400]	training's auc: 0.935973	valid_1's auc: 0.898331
[500]	training's auc: 0.941429	valid_1's auc: 0.900271
[600]	training's auc: 0.946233	valid_1's auc: 0.901588
[700]	training's auc: 0.950568	valid_1's auc: 0.902526
[800]	training's auc: 0.954484	valid_1's auc: 0.903165
[900]	training's auc: 0.95811	valid_1's auc: 0.903651
[1000]	training's auc: 0.961459	valid_1's auc: 0.904185
[1100]	training's auc: 0.9645	valid_1's auc: 0.90457
[1200]	training's auc: 0.967318	valid_1's auc: 0.904817
[1300]	training's auc: 0.969788	valid_1's auc: 0.90494
[1400]	training's auc: 0.972069	valid_1's auc: 0.904892
Early stopping, best iteration is:
[1333]	training's auc: 0.970567	valid_1's auc: 0.905003
Partial score of fold 1 is: 0.9050034148628702
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.911044	valid_1's auc: 0.883107
[200]	training's auc: 0.921754	valid_1's auc: 0.888583
[300]	training's auc: 0.929914	valid_1's auc: 0.893007
[400]	training's auc: 0.936475	valid_1's auc: 0.89575
[500]	training's auc: 0.941889	valid_1's auc: 0.897648
[600]	training's auc: 0.946607	valid_1's auc: 0.898832
[700]	training's auc: 0.950977	valid_1's auc: 0.899565
[800]	training's auc: 0.954944	valid_1's auc: 0.900132
[900]	training's auc: 0.958458	valid_1's auc: 0.900615
[1000]	training's auc: 0.961804	valid_1's auc: 0.90101
[1100]	training's auc: 0.964868	valid_1's auc: 0.901293
[1200]	training's auc: 0.967562	valid_1's auc: 0.901608
[1300]	training's auc: 0.970071	valid_1's auc: 0.901754
[1400]	training's auc: 0.972297	valid_1's auc: 0.901968
[1500]	training's auc: 0.974417	valid_1's auc: 0.902174
[1600]	training's auc: 0.976354	valid_1's auc: 0.902315
[1700]	training's auc: 0.978118	valid_1's auc: 0.90235
Early stopping, best iteration is:
[1685]	training's auc: 0.977871	valid_1's auc: 0.902373
Partial score of fold 2 is: 0.9023727266285428
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.910938	valid_1's auc: 0.887987
[200]	training's auc: 0.921307	valid_1's auc: 0.892677
[300]	training's auc: 0.929551	valid_1's auc: 0.896324
[400]	training's auc: 0.936153	valid_1's auc: 0.898571
[500]	training's auc: 0.941589	valid_1's auc: 0.900288
[600]	training's auc: 0.946379	valid_1's auc: 0.901567
[700]	training's auc: 0.950608	valid_1's auc: 0.902303
[800]	training's auc: 0.95472	valid_1's auc: 0.902869
[900]	training's auc: 0.958453	valid_1's auc: 0.903298
[1000]	training's auc: 0.961768	valid_1's auc: 0.903727
[1100]	training's auc: 0.964754	valid_1's auc: 0.904069
[1200]	training's auc: 0.9675	valid_1's auc: 0.904257
[1300]	training's auc: 0.969997	valid_1's auc: 0.904408
Early stopping, best iteration is:
[1285]	training's auc: 0.969623	valid_1's auc: 0.904458
Partial score of fold 3 is: 0.9044578945465871
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.910457	valid_1's auc: 0.883333
[200]	training's auc: 0.920638	valid_1's auc: 0.890055
[300]	training's auc: 0.928844	valid_1's auc: 0.895639
[400]	training's auc: 0.935515	valid_1's auc: 0.898907
[500]	training's auc: 0.941048	valid_1's auc: 0.901051
[600]	training's auc: 0.945851	valid_1's auc: 0.902404
[700]	training's auc: 0.95033	valid_1's auc: 0.903388
[800]	training's auc: 0.954346	valid_1's auc: 0.904072
[900]	training's auc: 0.958017	valid_1's auc: 0.904671
[1000]	training's auc: 0.961284	valid_1's auc: 0.905104
[1100]	training's auc: 0.964279	valid_1's auc: 0.905259
[1200]	training's auc: 0.967093	valid_1's auc: 0.905546
[1300]	training's auc: 0.969694	valid_1's auc: 0.905673
[1400]	training's auc: 0.972005	valid_1's auc: 0.905801
[1500]	training's auc: 0.974048	valid_1's auc: 0.905939
[1600]	training's auc: 0.976013	valid_1's auc: 0.905958
Early stopping, best iteration is:
[1511]	training's auc: 0.97426	valid_1's auc: 0.906008
Partial score of fold 4 is: 0.9060080181693607
Our oof AUC score is:  0.9049929969517136
auc:  0.9049929969517136
|  7        |  0.905    |  0.713    |  3.884    |  2.672    |  0.009373 |  14.89    |  7.059    |  3.298    |
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.908024	valid_1's auc: 0.884948
[200]	training's auc: 0.917803	valid_1's auc: 0.889454
[300]	training's auc: 0.925549	valid_1's auc: 0.893573
[400]	training's auc: 0.932076	valid_1's auc: 0.896423
[500]	training's auc: 0.937366	valid_1's auc: 0.898437
[600]	training's auc: 0.94197	valid_1's auc: 0.89992
[700]	training's auc: 0.946047	valid_1's auc: 0.901016
[800]	training's auc: 0.94962	valid_1's auc: 0.901851
[900]	training's auc: 0.952951	valid_1's auc: 0.902492
[1000]	training's auc: 0.956025	valid_1's auc: 0.903054
[1100]	training's auc: 0.958917	valid_1's auc: 0.903489
[1200]	training's auc: 0.961606	valid_1's auc: 0.903903
[1300]	training's auc: 0.964065	valid_1's auc: 0.904132
[1400]	training's auc: 0.966325	valid_1's auc: 0.904449
[1500]	training's auc: 0.968458	valid_1's auc: 0.904681
[1600]	training's auc: 0.970377	valid_1's auc: 0.904878
[1700]	training's auc: 0.972138	valid_1's auc: 0.905081
[1800]	training's auc: 0.973859	valid_1's auc: 0.905242
[1900]	training's auc: 0.975505	valid_1's auc: 0.905378
[2000]	training's auc: 0.976989	valid_1's auc: 0.905541
[2100]	training's auc: 0.97836	valid_1's auc: 0.90555
[2200]	training's auc: 0.979714	valid_1's auc: 0.905571
Early stopping, best iteration is:
[2136]	training's auc: 0.97884	valid_1's auc: 0.905599
Partial score of fold 0 is: 0.905598528216718
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.908467	valid_1's auc: 0.880925
[200]	training's auc: 0.9182	valid_1's auc: 0.886223
[300]	training's auc: 0.926152	valid_1's auc: 0.890916
[400]	training's auc: 0.932738	valid_1's auc: 0.894476
[500]	training's auc: 0.937983	valid_1's auc: 0.896713
[600]	training's auc: 0.94248	valid_1's auc: 0.898299
[700]	training's auc: 0.946476	valid_1's auc: 0.8995
[800]	training's auc: 0.950083	valid_1's auc: 0.900445
[900]	training's auc: 0.953429	valid_1's auc: 0.901089
[1000]	training's auc: 0.956553	valid_1's auc: 0.901533
[1100]	training's auc: 0.959449	valid_1's auc: 0.9019
[1200]	training's auc: 0.962161	valid_1's auc: 0.902271
[1300]	training's auc: 0.964619	valid_1's auc: 0.902474
[1400]	training's auc: 0.966838	valid_1's auc: 0.902724
[1500]	training's auc: 0.968987	valid_1's auc: 0.902945
[1600]	training's auc: 0.970938	valid_1's auc: 0.903065
[1700]	training's auc: 0.972754	valid_1's auc: 0.903242
[1800]	training's auc: 0.974451	valid_1's auc: 0.903398
[1900]	training's auc: 0.975999	valid_1's auc: 0.903504
[2000]	training's auc: 0.977479	valid_1's auc: 0.90362
[2100]	training's auc: 0.978879	valid_1's auc: 0.903714
[2200]	training's auc: 0.98016	valid_1's auc: 0.90373
[2300]	training's auc: 0.981402	valid_1's auc: 0.903801
Early stopping, best iteration is:
[2299]	training's auc: 0.981389	valid_1's auc: 0.903809
Partial score of fold 1 is: 0.9038087782329254
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.908474	valid_1's auc: 0.878654
[200]	training's auc: 0.918439	valid_1's auc: 0.88337
[300]	training's auc: 0.926378	valid_1's auc: 0.888184
[400]	training's auc: 0.932946	valid_1's auc: 0.891508
[500]	training's auc: 0.938184	valid_1's auc: 0.893892
[600]	training's auc: 0.942765	valid_1's auc: 0.895513
[700]	training's auc: 0.94681	valid_1's auc: 0.896721
[800]	training's auc: 0.950552	valid_1's auc: 0.89778
[900]	training's auc: 0.953868	valid_1's auc: 0.898323
[1000]	training's auc: 0.956936	valid_1's auc: 0.898812
[1100]	training's auc: 0.959813	valid_1's auc: 0.899225
[1200]	training's auc: 0.962432	valid_1's auc: 0.899649
[1300]	training's auc: 0.964838	valid_1's auc: 0.900015
[1400]	training's auc: 0.967097	valid_1's auc: 0.900225
[1500]	training's auc: 0.969185	valid_1's auc: 0.900424
[1600]	training's auc: 0.971172	valid_1's auc: 0.900672
[1700]	training's auc: 0.972985	valid_1's auc: 0.900851
[1800]	training's auc: 0.974649	valid_1's auc: 0.900836
Early stopping, best iteration is:
[1713]	training's auc: 0.973215	valid_1's auc: 0.900879
Partial score of fold 2 is: 0.900879006810126
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.908177	valid_1's auc: 0.884712
[200]	training's auc: 0.917946	valid_1's auc: 0.888625
[300]	training's auc: 0.925642	valid_1's auc: 0.891972
[400]	training's auc: 0.932197	valid_1's auc: 0.894999
[500]	training's auc: 0.9376	valid_1's auc: 0.897319
[600]	training's auc: 0.942259	valid_1's auc: 0.898725
[700]	training's auc: 0.946407	valid_1's auc: 0.899876
[800]	training's auc: 0.950054	valid_1's auc: 0.9008
[900]	training's auc: 0.953452	valid_1's auc: 0.901462
[1000]	training's auc: 0.95654	valid_1's auc: 0.901984
[1100]	training's auc: 0.959456	valid_1's auc: 0.902293
[1200]	training's auc: 0.962131	valid_1's auc: 0.902598
[1300]	training's auc: 0.964608	valid_1's auc: 0.902821
[1400]	training's auc: 0.966938	valid_1's auc: 0.903146
[1500]	training's auc: 0.969064	valid_1's auc: 0.903144
[1600]	training's auc: 0.971026	valid_1's auc: 0.903145
Early stopping, best iteration is:
[1513]	training's auc: 0.969331	valid_1's auc: 0.90319
Partial score of fold 3 is: 0.9031904751153027
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.908431	valid_1's auc: 0.879332
[200]	training's auc: 0.918008	valid_1's auc: 0.884886
[300]	training's auc: 0.925734	valid_1's auc: 0.889958
[400]	training's auc: 0.932165	valid_1's auc: 0.894183
[500]	training's auc: 0.937444	valid_1's auc: 0.897241
[600]	training's auc: 0.941923	valid_1's auc: 0.899079
[700]	training's auc: 0.946026	valid_1's auc: 0.900491
[800]	training's auc: 0.949697	valid_1's auc: 0.901604
[900]	training's auc: 0.953109	valid_1's auc: 0.902347
[1000]	training's auc: 0.95619	valid_1's auc: 0.902957
[1100]	training's auc: 0.959107	valid_1's auc: 0.903363
[1200]	training's auc: 0.961784	valid_1's auc: 0.90381
[1300]	training's auc: 0.964306	valid_1's auc: 0.904082
[1400]	training's auc: 0.966555	valid_1's auc: 0.904213
[1500]	training's auc: 0.968699	valid_1's auc: 0.904434
[1600]	training's auc: 0.970688	valid_1's auc: 0.904517
[1700]	training's auc: 0.972563	valid_1's auc: 0.904712
[1800]	training's auc: 0.974206	valid_1's auc: 0.904915
[1900]	training's auc: 0.975766	valid_1's auc: 0.905044
[2000]	training's auc: 0.977215	valid_1's auc: 0.905078
[2100]	training's auc: 0.978628	valid_1's auc: 0.905125
[2200]	training's auc: 0.979947	valid_1's auc: 0.905255
Early stopping, best iteration is:
[2192]	training's auc: 0.979852	valid_1's auc: 0.905273
Partial score of fold 4 is: 0.9052725726386746
Our oof AUC score is:  0.9036389707541601
auc:  0.9036389707541601
|  8        |  0.9036   |  0.8202   |  3.754    |  1.866    |  0.007379 |  16.71    |  9.988    |  1.947    |
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.914893	valid_1's auc: 0.8901
[200]	training's auc: 0.931726	valid_1's auc: 0.897994
[300]	training's auc: 0.942469	valid_1's auc: 0.901494
[400]	training's auc: 0.951437	valid_1's auc: 0.903327
[500]	training's auc: 0.959043	valid_1's auc: 0.904429
[600]	training's auc: 0.964981	valid_1's auc: 0.905184
[700]	training's auc: 0.970136	valid_1's auc: 0.905463
[800]	training's auc: 0.974611	valid_1's auc: 0.905923
[900]	training's auc: 0.97837	valid_1's auc: 0.905949
[1000]	training's auc: 0.981618	valid_1's auc: 0.906011
[1100]	training's auc: 0.984328	valid_1's auc: 0.906062
Early stopping, best iteration is:
[1026]	training's auc: 0.982329	valid_1's auc: 0.906218
Partial score of fold 0 is: 0.9062182165219057
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.914513	valid_1's auc: 0.888507
[200]	training's auc: 0.931208	valid_1's auc: 0.896006
[300]	training's auc: 0.942548	valid_1's auc: 0.899966
[400]	training's auc: 0.95128	valid_1's auc: 0.901739
[500]	training's auc: 0.95903	valid_1's auc: 0.902774
[600]	training's auc: 0.965366	valid_1's auc: 0.903556
[700]	training's auc: 0.970408	valid_1's auc: 0.903764
[800]	training's auc: 0.975129	valid_1's auc: 0.904113
[900]	training's auc: 0.979034	valid_1's auc: 0.904003
Early stopping, best iteration is:
[863]	training's auc: 0.977773	valid_1's auc: 0.904243
Partial score of fold 1 is: 0.904243136728697
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.915726	valid_1's auc: 0.886524
[200]	training's auc: 0.932266	valid_1's auc: 0.894821
[300]	training's auc: 0.943092	valid_1's auc: 0.897805
[400]	training's auc: 0.951911	valid_1's auc: 0.899271
[500]	training's auc: 0.959816	valid_1's auc: 0.900163
[600]	training's auc: 0.965826	valid_1's auc: 0.900423
[700]	training's auc: 0.971173	valid_1's auc: 0.900471
[800]	training's auc: 0.975594	valid_1's auc: 0.901163
[900]	training's auc: 0.979165	valid_1's auc: 0.901273
[1000]	training's auc: 0.98234	valid_1's auc: 0.901282
Early stopping, best iteration is:
[934]	training's auc: 0.980373	valid_1's auc: 0.901449
Partial score of fold 2 is: 0.9014487536881273
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.914959	valid_1's auc: 0.890688
[200]	training's auc: 0.931782	valid_1's auc: 0.897892
[300]	training's auc: 0.942911	valid_1's auc: 0.900301
[400]	training's auc: 0.952016	valid_1's auc: 0.901938
[500]	training's auc: 0.959399	valid_1's auc: 0.903083
[600]	training's auc: 0.965773	valid_1's auc: 0.903557
[700]	training's auc: 0.971025	valid_1's auc: 0.90356
Early stopping, best iteration is:
[681]	training's auc: 0.970058	valid_1's auc: 0.903685
Partial score of fold 3 is: 0.9036845289603493
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.914822	valid_1's auc: 0.888179
[200]	training's auc: 0.932284	valid_1's auc: 0.897906
[300]	training's auc: 0.943444	valid_1's auc: 0.90087
[400]	training's auc: 0.95178	valid_1's auc: 0.902373
[500]	training's auc: 0.959731	valid_1's auc: 0.903271
[600]	training's auc: 0.965527	valid_1's auc: 0.903767
[700]	training's auc: 0.971112	valid_1's auc: 0.904001
[800]	training's auc: 0.975531	valid_1's auc: 0.904454
[900]	training's auc: 0.979218	valid_1's auc: 0.904632
[1000]	training's auc: 0.982327	valid_1's auc: 0.904918
[1100]	training's auc: 0.984991	valid_1's auc: 0.904862
Early stopping, best iteration is:
[1002]	training's auc: 0.982398	valid_1's auc: 0.904956
Partial score of fold 4 is: 0.9049563962709131
Our oof AUC score is:  0.9040562573563592
auc:  0.9040562573563592
|  9        |  0.9041   |  0.9569   |  1.553    |  2.381    |  0.01897  |  16.49    |  1.495    |  9.698    |
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.91541	valid_1's auc: 0.889443
[200]	training's auc: 0.92937	valid_1's auc: 0.896249
[300]	training's auc: 0.938849	valid_1's auc: 0.899573
[400]	training's auc: 0.946334	valid_1's auc: 0.901761
[500]	training's auc: 0.952719	valid_1's auc: 0.902951
[600]	training's auc: 0.958112	valid_1's auc: 0.9038
[700]	training's auc: 0.963004	valid_1's auc: 0.904322
[800]	training's auc: 0.967201	valid_1's auc: 0.904759
[900]	training's auc: 0.970785	valid_1's auc: 0.905116
[1000]	training's auc: 0.974005	valid_1's auc: 0.905272
Early stopping, best iteration is:
[960]	training's auc: 0.972753	valid_1's auc: 0.905291
Partial score of fold 0 is: 0.9052905121088173
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.915198	valid_1's auc: 0.885519
[200]	training's auc: 0.929686	valid_1's auc: 0.892923
[300]	training's auc: 0.939402	valid_1's auc: 0.896901
[400]	training's auc: 0.946861	valid_1's auc: 0.89886
[500]	training's auc: 0.953206	valid_1's auc: 0.900178
[600]	training's auc: 0.958677	valid_1's auc: 0.901041
[700]	training's auc: 0.963579	valid_1's auc: 0.901805
[800]	training's auc: 0.967693	valid_1's auc: 0.902109
[900]	training's auc: 0.971357	valid_1's auc: 0.902277
[1000]	training's auc: 0.974639	valid_1's auc: 0.902303
[1100]	training's auc: 0.977595	valid_1's auc: 0.902444
[1200]	training's auc: 0.980126	valid_1's auc: 0.902558
[1300]	training's auc: 0.982311	valid_1's auc: 0.90249
Early stopping, best iteration is:
[1215]	training's auc: 0.980467	valid_1's auc: 0.902595
Partial score of fold 1 is: 0.9025954842396164
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.91607	valid_1's auc: 0.883814
[200]	training's auc: 0.930493	valid_1's auc: 0.891104
[300]	training's auc: 0.939888	valid_1's auc: 0.894817
[400]	training's auc: 0.947366	valid_1's auc: 0.896471
[500]	training's auc: 0.953665	valid_1's auc: 0.897773
[600]	training's auc: 0.959087	valid_1's auc: 0.898376
[700]	training's auc: 0.96379	valid_1's auc: 0.89903
[800]	training's auc: 0.968116	valid_1's auc: 0.899405
[900]	training's auc: 0.971547	valid_1's auc: 0.89978
[1000]	training's auc: 0.974828	valid_1's auc: 0.900108
[1100]	training's auc: 0.977536	valid_1's auc: 0.900313
[1200]	training's auc: 0.98004	valid_1's auc: 0.900337
[1300]	training's auc: 0.982352	valid_1's auc: 0.900584
[1400]	training's auc: 0.984328	valid_1's auc: 0.900725
[1500]	training's auc: 0.986116	valid_1's auc: 0.900826
[1600]	training's auc: 0.987705	valid_1's auc: 0.900924
Early stopping, best iteration is:
[1593]	training's auc: 0.987601	valid_1's auc: 0.900959
Partial score of fold 2 is: 0.9009594030849856
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.915631	valid_1's auc: 0.889465
[200]	training's auc: 0.929756	valid_1's auc: 0.895064
[300]	training's auc: 0.939542	valid_1's auc: 0.898338
[400]	training's auc: 0.947174	valid_1's auc: 0.900211
[500]	training's auc: 0.953504	valid_1's auc: 0.901375
[600]	training's auc: 0.959059	valid_1's auc: 0.901944
[700]	training's auc: 0.963819	valid_1's auc: 0.902355
[800]	training's auc: 0.967936	valid_1's auc: 0.902611
[900]	training's auc: 0.971614	valid_1's auc: 0.902996
[1000]	training's auc: 0.97475	valid_1's auc: 0.903265
[1100]	training's auc: 0.977564	valid_1's auc: 0.903406
[1200]	training's auc: 0.979957	valid_1's auc: 0.903391
Early stopping, best iteration is:
[1161]	training's auc: 0.979043	valid_1's auc: 0.903523
Partial score of fold 3 is: 0.9035227467750332
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.915389	valid_1's auc: 0.884536
[200]	training's auc: 0.929332	valid_1's auc: 0.893679
[300]	training's auc: 0.938955	valid_1's auc: 0.89829
[400]	training's auc: 0.946527	valid_1's auc: 0.900606
[500]	training's auc: 0.952832	valid_1's auc: 0.901984
[600]	training's auc: 0.958306	valid_1's auc: 0.902908
[700]	training's auc: 0.963365	valid_1's auc: 0.903302
[800]	training's auc: 0.967488	valid_1's auc: 0.903618
[900]	training's auc: 0.971204	valid_1's auc: 0.903794
[1000]	training's auc: 0.974464	valid_1's auc: 0.903908
[1100]	training's auc: 0.977122	valid_1's auc: 0.904231
[1200]	training's auc: 0.979614	valid_1's auc: 0.904263
[1300]	training's auc: 0.981997	valid_1's auc: 0.904211
Early stopping, best iteration is:
[1203]	training's auc: 0.979688	valid_1's auc: 0.90429
Partial score of fold 4 is: 0.9042903451957645
Our oof AUC score is:  0.903116469188148
auc:  0.903116469188148
|  10       |  0.9031   |  0.9777   |  0.3474   |  2.403    |  0.01265  |  14.47    |  4.909    |  3.499    |
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.910315	valid_1's auc: 0.888552
[200]	training's auc: 0.920593	valid_1's auc: 0.893777
[300]	training's auc: 0.928667	valid_1's auc: 0.897441
[400]	training's auc: 0.935023	valid_1's auc: 0.899898
[500]	training's auc: 0.940483	valid_1's auc: 0.901491
[600]	training's auc: 0.945197	valid_1's auc: 0.902627
[700]	training's auc: 0.949638	valid_1's auc: 0.903415
[800]	training's auc: 0.953728	valid_1's auc: 0.90426
[900]	training's auc: 0.95746	valid_1's auc: 0.904602
[1000]	training's auc: 0.960782	valid_1's auc: 0.90512
[1100]	training's auc: 0.963804	valid_1's auc: 0.905482
[1200]	training's auc: 0.96674	valid_1's auc: 0.905799
[1300]	training's auc: 0.969334	valid_1's auc: 0.906042
[1400]	training's auc: 0.971683	valid_1's auc: 0.906179
[1500]	training's auc: 0.973885	valid_1's auc: 0.906423
[1600]	training's auc: 0.975789	valid_1's auc: 0.906702
[1700]	training's auc: 0.977575	valid_1's auc: 0.90675
[1800]	training's auc: 0.979286	valid_1's auc: 0.906935
[1900]	training's auc: 0.980887	valid_1's auc: 0.907133
[2000]	training's auc: 0.982327	valid_1's auc: 0.907216
[2100]	training's auc: 0.983594	valid_1's auc: 0.907099
Early stopping, best iteration is:
[2033]	training's auc: 0.982766	valid_1's auc: 0.907308
Partial score of fold 0 is: 0.9073082211504394
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.910845	valid_1's auc: 0.886555
[200]	training's auc: 0.920825	valid_1's auc: 0.89216
[300]	training's auc: 0.928994	valid_1's auc: 0.896389
[400]	training's auc: 0.935411	valid_1's auc: 0.899008
[500]	training's auc: 0.940917	valid_1's auc: 0.900697
[600]	training's auc: 0.945802	valid_1's auc: 0.90208
[700]	training's auc: 0.950168	valid_1's auc: 0.902999
[800]	training's auc: 0.954135	valid_1's auc: 0.903665
[900]	training's auc: 0.957883	valid_1's auc: 0.904179
[1000]	training's auc: 0.961426	valid_1's auc: 0.904448
[1100]	training's auc: 0.964517	valid_1's auc: 0.90469
[1200]	training's auc: 0.967323	valid_1's auc: 0.904838
[1300]	training's auc: 0.969826	valid_1's auc: 0.905007
[1400]	training's auc: 0.972026	valid_1's auc: 0.905135
Early stopping, best iteration is:
[1349]	training's auc: 0.970877	valid_1's auc: 0.905224
Partial score of fold 1 is: 0.9052239863586404
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.910641	valid_1's auc: 0.883975
[200]	training's auc: 0.921155	valid_1's auc: 0.889613
[300]	training's auc: 0.929484	valid_1's auc: 0.894077
[400]	training's auc: 0.935825	valid_1's auc: 0.896776
[500]	training's auc: 0.941349	valid_1's auc: 0.898639
[600]	training's auc: 0.946117	valid_1's auc: 0.899645
[700]	training's auc: 0.950534	valid_1's auc: 0.900386
[800]	training's auc: 0.954484	valid_1's auc: 0.901043
[900]	training's auc: 0.958145	valid_1's auc: 0.901469
[1000]	training's auc: 0.961581	valid_1's auc: 0.90185
[1100]	training's auc: 0.964687	valid_1's auc: 0.902045
[1200]	training's auc: 0.967449	valid_1's auc: 0.902316
[1300]	training's auc: 0.969931	valid_1's auc: 0.902669
[1400]	training's auc: 0.972262	valid_1's auc: 0.90279
[1500]	training's auc: 0.974319	valid_1's auc: 0.902882
[1600]	training's auc: 0.976306	valid_1's auc: 0.902871
Early stopping, best iteration is:
[1538]	training's auc: 0.975058	valid_1's auc: 0.903002
Partial score of fold 2 is: 0.903002440910811
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.910836	valid_1's auc: 0.88858
[200]	training's auc: 0.920756	valid_1's auc: 0.893269
[300]	training's auc: 0.929003	valid_1's auc: 0.896853
[400]	training's auc: 0.935566	valid_1's auc: 0.899236
[500]	training's auc: 0.940943	valid_1's auc: 0.900998
[600]	training's auc: 0.945845	valid_1's auc: 0.902211
[700]	training's auc: 0.950259	valid_1's auc: 0.902989
[800]	training's auc: 0.954392	valid_1's auc: 0.903522
[900]	training's auc: 0.958166	valid_1's auc: 0.903919
[1000]	training's auc: 0.961513	valid_1's auc: 0.904501
[1100]	training's auc: 0.964553	valid_1's auc: 0.904938
[1200]	training's auc: 0.967447	valid_1's auc: 0.905102
[1300]	training's auc: 0.970023	valid_1's auc: 0.90519
[1400]	training's auc: 0.972253	valid_1's auc: 0.905366
[1500]	training's auc: 0.97442	valid_1's auc: 0.905362
Early stopping, best iteration is:
[1435]	training's auc: 0.972989	valid_1's auc: 0.905431
Partial score of fold 3 is: 0.9054313016187228
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.910445	valid_1's auc: 0.885466
[200]	training's auc: 0.920701	valid_1's auc: 0.891591
[300]	training's auc: 0.928756	valid_1's auc: 0.896678
[400]	training's auc: 0.935218	valid_1's auc: 0.899864
[500]	training's auc: 0.940694	valid_1's auc: 0.901974
[600]	training's auc: 0.945588	valid_1's auc: 0.903403
[700]	training's auc: 0.950237	valid_1's auc: 0.904369
[800]	training's auc: 0.954173	valid_1's auc: 0.90507
[900]	training's auc: 0.957824	valid_1's auc: 0.905573
[1000]	training's auc: 0.961264	valid_1's auc: 0.906121
[1100]	training's auc: 0.964253	valid_1's auc: 0.906361
[1200]	training's auc: 0.967043	valid_1's auc: 0.906676
[1300]	training's auc: 0.969572	valid_1's auc: 0.906917
[1400]	training's auc: 0.971956	valid_1's auc: 0.907019
Early stopping, best iteration is:
[1388]	training's auc: 0.971657	valid_1's auc: 0.907051
Partial score of fold 4 is: 0.9070511212481713
Our oof AUC score is:  0.9054503552499539
auc:  0.9054503552499539
|  11       |  0.9055   |  0.6576   |  3.07     |  1.994    |  0.009464 |  15.7     |  5.839    |  9.55     |
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.92156	valid_1's auc: 0.893148
[200]	training's auc: 0.93646	valid_1's auc: 0.899328
[300]	training's auc: 0.946846	valid_1's auc: 0.902223
[400]	training's auc: 0.955245	valid_1's auc: 0.903553
[500]	training's auc: 0.962122	valid_1's auc: 0.904034
[600]	training's auc: 0.967964	valid_1's auc: 0.904759
[700]	training's auc: 0.972879	valid_1's auc: 0.905053
Early stopping, best iteration is:
[690]	training's auc: 0.972443	valid_1's auc: 0.905176
Partial score of fold 0 is: 0.905176193355114
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.921676	valid_1's auc: 0.891186
[200]	training's auc: 0.936929	valid_1's auc: 0.898561
[300]	training's auc: 0.94722	valid_1's auc: 0.901891
[400]	training's auc: 0.955637	valid_1's auc: 0.902909
[500]	training's auc: 0.962457	valid_1's auc: 0.903862
[600]	training's auc: 0.968234	valid_1's auc: 0.904396
[700]	training's auc: 0.972772	valid_1's auc: 0.904468
[800]	training's auc: 0.97704	valid_1's auc: 0.904598
[900]	training's auc: 0.980582	valid_1's auc: 0.904695
[1000]	training's auc: 0.983389	valid_1's auc: 0.904433
Early stopping, best iteration is:
[908]	training's auc: 0.980846	valid_1's auc: 0.904723
Partial score of fold 1 is: 0.9047227628879035
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.921915	valid_1's auc: 0.888279
[200]	training's auc: 0.936733	valid_1's auc: 0.895282
[300]	training's auc: 0.947401	valid_1's auc: 0.898367
[400]	training's auc: 0.955936	valid_1's auc: 0.899591
[500]	training's auc: 0.962766	valid_1's auc: 0.900809
[600]	training's auc: 0.968481	valid_1's auc: 0.901337
[700]	training's auc: 0.973332	valid_1's auc: 0.901572
[800]	training's auc: 0.977394	valid_1's auc: 0.901972
[900]	training's auc: 0.980667	valid_1's auc: 0.9021
Early stopping, best iteration is:
[885]	training's auc: 0.980211	valid_1's auc: 0.902184
Partial score of fold 2 is: 0.9021840422628857
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.921221	valid_1's auc: 0.891874
[200]	training's auc: 0.936419	valid_1's auc: 0.898581
[300]	training's auc: 0.946881	valid_1's auc: 0.901226
[400]	training's auc: 0.955322	valid_1's auc: 0.902574
[500]	training's auc: 0.962144	valid_1's auc: 0.90368
[600]	training's auc: 0.967984	valid_1's auc: 0.904375
[700]	training's auc: 0.972829	valid_1's auc: 0.904719
Early stopping, best iteration is:
[688]	training's auc: 0.97225	valid_1's auc: 0.904823
Partial score of fold 3 is: 0.9048233745253443
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.921305	valid_1's auc: 0.889549
[200]	training's auc: 0.936558	valid_1's auc: 0.89923
[300]	training's auc: 0.946907	valid_1's auc: 0.902381
[400]	training's auc: 0.955223	valid_1's auc: 0.903971
[500]	training's auc: 0.962193	valid_1's auc: 0.905011
[600]	training's auc: 0.96809	valid_1's auc: 0.905301
[700]	training's auc: 0.97265	valid_1's auc: 0.905721
[800]	training's auc: 0.976832	valid_1's auc: 0.905863
[900]	training's auc: 0.980377	valid_1's auc: 0.905773
Early stopping, best iteration is:
[803]	training's auc: 0.976951	valid_1's auc: 0.905872
Partial score of fold 4 is: 0.9058716416673862
Our oof AUC score is:  0.9044347688859878
auc:  0.9044347688859878
|  12       |  0.9044   |  0.7344   |  1.269    |  0.9706   |  0.01855  |  14.4     |  7.089    |  5.414    |
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.91091	valid_1's auc: 0.886528
[200]	training's auc: 0.921765	valid_1's auc: 0.891898
[300]	training's auc: 0.930413	valid_1's auc: 0.896194
[400]	training's auc: 0.937092	valid_1's auc: 0.898555
[500]	training's auc: 0.942633	valid_1's auc: 0.900158
[600]	training's auc: 0.947535	valid_1's auc: 0.901477
[700]	training's auc: 0.951977	valid_1's auc: 0.902387
[800]	training's auc: 0.955899	valid_1's auc: 0.9032
[900]	training's auc: 0.959617	valid_1's auc: 0.903793
[1000]	training's auc: 0.962883	valid_1's auc: 0.904234
[1100]	training's auc: 0.965893	valid_1's auc: 0.904569
[1200]	training's auc: 0.968629	valid_1's auc: 0.904881
[1300]	training's auc: 0.971091	valid_1's auc: 0.905082
[1400]	training's auc: 0.973324	valid_1's auc: 0.905123
[1500]	training's auc: 0.975468	valid_1's auc: 0.905302
[1600]	training's auc: 0.977396	valid_1's auc: 0.905474
[1700]	training's auc: 0.979134	valid_1's auc: 0.905489
Early stopping, best iteration is:
[1653]	training's auc: 0.978372	valid_1's auc: 0.905568
Partial score of fold 0 is: 0.9055681487523314
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.911245	valid_1's auc: 0.883818
[200]	training's auc: 0.922259	valid_1's auc: 0.889493
[300]	training's auc: 0.931077	valid_1's auc: 0.894366
[400]	training's auc: 0.937778	valid_1's auc: 0.897091
[500]	training's auc: 0.943292	valid_1's auc: 0.898832
[600]	training's auc: 0.948251	valid_1's auc: 0.900081
[700]	training's auc: 0.952566	valid_1's auc: 0.901138
[800]	training's auc: 0.956482	valid_1's auc: 0.901893
[900]	training's auc: 0.960042	valid_1's auc: 0.902439
[1000]	training's auc: 0.963365	valid_1's auc: 0.90266
[1100]	training's auc: 0.96631	valid_1's auc: 0.902778
[1200]	training's auc: 0.969102	valid_1's auc: 0.903099
[1300]	training's auc: 0.971562	valid_1's auc: 0.903092
[1400]	training's auc: 0.973751	valid_1's auc: 0.90331
[1500]	training's auc: 0.975911	valid_1's auc: 0.903495
[1600]	training's auc: 0.977827	valid_1's auc: 0.903582
[1700]	training's auc: 0.979611	valid_1's auc: 0.903671
[1800]	training's auc: 0.981229	valid_1's auc: 0.903803
[1900]	training's auc: 0.982714	valid_1's auc: 0.903852
[2000]	training's auc: 0.984088	valid_1's auc: 0.903828
Early stopping, best iteration is:
[1939]	training's auc: 0.983263	valid_1's auc: 0.90394
Partial score of fold 1 is: 0.9039395682346892
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.91199	valid_1's auc: 0.881638
[200]	training's auc: 0.92297	valid_1's auc: 0.886962
[300]	training's auc: 0.931402	valid_1's auc: 0.89151
[400]	training's auc: 0.938097	valid_1's auc: 0.89415
[500]	training's auc: 0.943635	valid_1's auc: 0.896153
[600]	training's auc: 0.948505	valid_1's auc: 0.897523
[700]	training's auc: 0.952914	valid_1's auc: 0.898407
[800]	training's auc: 0.956859	valid_1's auc: 0.899119
[900]	training's auc: 0.960303	valid_1's auc: 0.899607
[1000]	training's auc: 0.963643	valid_1's auc: 0.899814
[1100]	training's auc: 0.966629	valid_1's auc: 0.900021
[1200]	training's auc: 0.969333	valid_1's auc: 0.900208
[1300]	training's auc: 0.971762	valid_1's auc: 0.900453
[1400]	training's auc: 0.973984	valid_1's auc: 0.900733
[1500]	training's auc: 0.976099	valid_1's auc: 0.900817
[1600]	training's auc: 0.978011	valid_1's auc: 0.900898
[1700]	training's auc: 0.979755	valid_1's auc: 0.901008
[1800]	training's auc: 0.981375	valid_1's auc: 0.90094
Early stopping, best iteration is:
[1718]	training's auc: 0.980062	valid_1's auc: 0.901016
Partial score of fold 2 is: 0.9010158274747955
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.911163	valid_1's auc: 0.887642
[200]	training's auc: 0.922136	valid_1's auc: 0.891636
[300]	training's auc: 0.930774	valid_1's auc: 0.895203
[400]	training's auc: 0.937562	valid_1's auc: 0.897651
[500]	training's auc: 0.943279	valid_1's auc: 0.899401
[600]	training's auc: 0.948177	valid_1's auc: 0.900646
[700]	training's auc: 0.952489	valid_1's auc: 0.901432
[800]	training's auc: 0.956565	valid_1's auc: 0.902035
[900]	training's auc: 0.960158	valid_1's auc: 0.90262
[1000]	training's auc: 0.963419	valid_1's auc: 0.903139
[1100]	training's auc: 0.966375	valid_1's auc: 0.903583
[1200]	training's auc: 0.969135	valid_1's auc: 0.90376
Early stopping, best iteration is:
[1193]	training's auc: 0.968978	valid_1's auc: 0.903805
Partial score of fold 3 is: 0.9038046593947862
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.910948	valid_1's auc: 0.881781
[200]	training's auc: 0.921938	valid_1's auc: 0.888547
[300]	training's auc: 0.930597	valid_1's auc: 0.893936
[400]	training's auc: 0.937334	valid_1's auc: 0.897473
[500]	training's auc: 0.943007	valid_1's auc: 0.899477
[600]	training's auc: 0.947957	valid_1's auc: 0.900848
[700]	training's auc: 0.952413	valid_1's auc: 0.901833
[800]	training's auc: 0.956366	valid_1's auc: 0.902575
[900]	training's auc: 0.959941	valid_1's auc: 0.903092
[1000]	training's auc: 0.963142	valid_1's auc: 0.903477
[1100]	training's auc: 0.96613	valid_1's auc: 0.90371
[1200]	training's auc: 0.9688	valid_1's auc: 0.904003
[1300]	training's auc: 0.971296	valid_1's auc: 0.904169
[1400]	training's auc: 0.973597	valid_1's auc: 0.904325
Early stopping, best iteration is:
[1368]	training's auc: 0.972893	valid_1's auc: 0.904384
Partial score of fold 4 is: 0.9043840145178779
Our oof AUC score is:  0.9035179912612705
auc:  0.9035179912612705
|  13       |  0.9035   |  0.853    |  1.536    |  0.4828   |  0.008905 |  14.28    |  7.365    |  3.389    |
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.907685	valid_1's auc: 0.890305
[200]	training's auc: 0.919754	valid_1's auc: 0.895815
[300]	training's auc: 0.928694	valid_1's auc: 0.899999
[400]	training's auc: 0.935669	valid_1's auc: 0.902551
[500]	training's auc: 0.94158	valid_1's auc: 0.904156
[600]	training's auc: 0.947046	valid_1's auc: 0.905534
[700]	training's auc: 0.952092	valid_1's auc: 0.906383
[800]	training's auc: 0.95666	valid_1's auc: 0.907206
[900]	training's auc: 0.960705	valid_1's auc: 0.907687
[1000]	training's auc: 0.964291	valid_1's auc: 0.908053
[1100]	training's auc: 0.967646	valid_1's auc: 0.908322
[1200]	training's auc: 0.970728	valid_1's auc: 0.908579
[1300]	training's auc: 0.973502	valid_1's auc: 0.908796
[1400]	training's auc: 0.97593	valid_1's auc: 0.909007
[1500]	training's auc: 0.978109	valid_1's auc: 0.908999
[1600]	training's auc: 0.980117	valid_1's auc: 0.909053
Early stopping, best iteration is:
[1592]	training's auc: 0.979984	valid_1's auc: 0.909108
Partial score of fold 0 is: 0.9091081478778852
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.907705	valid_1's auc: 0.887974
[200]	training's auc: 0.919669	valid_1's auc: 0.893795
[300]	training's auc: 0.928798	valid_1's auc: 0.898481
[400]	training's auc: 0.935728	valid_1's auc: 0.901182
[500]	training's auc: 0.941731	valid_1's auc: 0.902988
[600]	training's auc: 0.9472	valid_1's auc: 0.904244
[700]	training's auc: 0.952172	valid_1's auc: 0.904956
[800]	training's auc: 0.956668	valid_1's auc: 0.905444
[900]	training's auc: 0.960849	valid_1's auc: 0.905833
[1000]	training's auc: 0.964426	valid_1's auc: 0.906193
[1100]	training's auc: 0.967796	valid_1's auc: 0.906432
[1200]	training's auc: 0.970925	valid_1's auc: 0.906771
[1300]	training's auc: 0.973655	valid_1's auc: 0.906893
[1400]	training's auc: 0.97609	valid_1's auc: 0.907084
[1500]	training's auc: 0.978382	valid_1's auc: 0.907038
Early stopping, best iteration is:
[1440]	training's auc: 0.977033	valid_1's auc: 0.907127
Partial score of fold 1 is: 0.9071267358886258
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.908889	valid_1's auc: 0.884628
[200]	training's auc: 0.920462	valid_1's auc: 0.890885
[300]	training's auc: 0.929438	valid_1's auc: 0.895515
[400]	training's auc: 0.936501	valid_1's auc: 0.898009
[500]	training's auc: 0.942618	valid_1's auc: 0.89955
[600]	training's auc: 0.948077	valid_1's auc: 0.900457
[700]	training's auc: 0.952987	valid_1's auc: 0.901234
[800]	training's auc: 0.957445	valid_1's auc: 0.901937
[900]	training's auc: 0.961532	valid_1's auc: 0.902318
[1000]	training's auc: 0.965037	valid_1's auc: 0.902687
[1100]	training's auc: 0.968318	valid_1's auc: 0.902993
[1200]	training's auc: 0.971315	valid_1's auc: 0.90314
[1300]	training's auc: 0.974016	valid_1's auc: 0.903286
[1400]	training's auc: 0.976456	valid_1's auc: 0.90343
[1500]	training's auc: 0.978675	valid_1's auc: 0.903564
[1600]	training's auc: 0.980739	valid_1's auc: 0.903652
[1700]	training's auc: 0.982591	valid_1's auc: 0.903816
[1800]	training's auc: 0.984265	valid_1's auc: 0.90379
Early stopping, best iteration is:
[1717]	training's auc: 0.982886	valid_1's auc: 0.90385
Partial score of fold 2 is: 0.903849522899181
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.908164	valid_1's auc: 0.890754
[200]	training's auc: 0.919764	valid_1's auc: 0.895874
[300]	training's auc: 0.929093	valid_1's auc: 0.899549
[400]	training's auc: 0.936238	valid_1's auc: 0.901919
[500]	training's auc: 0.942251	valid_1's auc: 0.90308
[600]	training's auc: 0.94763	valid_1's auc: 0.903757
[700]	training's auc: 0.952635	valid_1's auc: 0.90447
[800]	training's auc: 0.957173	valid_1's auc: 0.905023
[900]	training's auc: 0.961428	valid_1's auc: 0.905372
[1000]	training's auc: 0.964922	valid_1's auc: 0.905712
[1100]	training's auc: 0.968182	valid_1's auc: 0.905957
[1200]	training's auc: 0.971173	valid_1's auc: 0.906114
Early stopping, best iteration is:
[1185]	training's auc: 0.970722	valid_1's auc: 0.906145
Partial score of fold 3 is: 0.9061450354675022
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.90763	valid_1's auc: 0.888399
[200]	training's auc: 0.919563	valid_1's auc: 0.894806
[300]	training's auc: 0.928634	valid_1's auc: 0.899939
[400]	training's auc: 0.935809	valid_1's auc: 0.902952
[500]	training's auc: 0.94191	valid_1's auc: 0.904626
[600]	training's auc: 0.947339	valid_1's auc: 0.905661
[700]	training's auc: 0.952259	valid_1's auc: 0.906229
[800]	training's auc: 0.956797	valid_1's auc: 0.906929
[900]	training's auc: 0.961012	valid_1's auc: 0.907384
[1000]	training's auc: 0.964664	valid_1's auc: 0.907709
[1100]	training's auc: 0.967961	valid_1's auc: 0.90796
[1200]	training's auc: 0.971058	valid_1's auc: 0.908108
[1300]	training's auc: 0.9738	valid_1's auc: 0.908131
[1400]	training's auc: 0.976283	valid_1's auc: 0.908282
[1500]	training's auc: 0.978477	valid_1's auc: 0.908281
[1600]	training's auc: 0.980524	valid_1's auc: 0.908308
[1700]	training's auc: 0.982439	valid_1's auc: 0.908441
Early stopping, best iteration is:
[1699]	training's auc: 0.982424	valid_1's auc: 0.908451
Partial score of fold 4 is: 0.9084505069960429
Our oof AUC score is:  0.9068646608695636
auc:  0.9068646608695636
|  14       |  0.9069   |  0.6826   |  1.33     |  2.889    |  0.01166  |  16.0     |  1.74     |  2.542    |
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.917446	valid_1's auc: 0.891603
[200]	training's auc: 0.930516	valid_1's auc: 0.898161
[300]	training's auc: 0.940072	valid_1's auc: 0.901545
[400]	training's auc: 0.948071	valid_1's auc: 0.90341
[500]	training's auc: 0.954605	valid_1's auc: 0.904575
[600]	training's auc: 0.959994	valid_1's auc: 0.905486
[700]	training's auc: 0.964925	valid_1's auc: 0.906143
[800]	training's auc: 0.969335	valid_1's auc: 0.906557
[900]	training's auc: 0.973007	valid_1's auc: 0.906638
[1000]	training's auc: 0.97631	valid_1's auc: 0.906799
[1100]	training's auc: 0.979197	valid_1's auc: 0.907005
[1200]	training's auc: 0.981709	valid_1's auc: 0.907133
[1300]	training's auc: 0.983932	valid_1's auc: 0.907073
Early stopping, best iteration is:
[1232]	training's auc: 0.982428	valid_1's auc: 0.907161
Partial score of fold 0 is: 0.9071612237421168
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.917772	valid_1's auc: 0.89061
[200]	training's auc: 0.930722	valid_1's auc: 0.897248
[300]	training's auc: 0.940642	valid_1's auc: 0.90109
[400]	training's auc: 0.948541	valid_1's auc: 0.903044
[500]	training's auc: 0.955201	valid_1's auc: 0.904251
[600]	training's auc: 0.960987	valid_1's auc: 0.905027
[700]	training's auc: 0.96578	valid_1's auc: 0.905319
[800]	training's auc: 0.970037	valid_1's auc: 0.905575
[900]	training's auc: 0.973772	valid_1's auc: 0.905697
Early stopping, best iteration is:
[873]	training's auc: 0.9728	valid_1's auc: 0.90584
Partial score of fold 1 is: 0.9058399054995124
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.918164	valid_1's auc: 0.887158
[200]	training's auc: 0.931542	valid_1's auc: 0.894782
[300]	training's auc: 0.941054	valid_1's auc: 0.898573
[400]	training's auc: 0.948889	valid_1's auc: 0.900304
[500]	training's auc: 0.955421	valid_1's auc: 0.90142
[600]	training's auc: 0.96078	valid_1's auc: 0.902278
[700]	training's auc: 0.965827	valid_1's auc: 0.90227
Early stopping, best iteration is:
[605]	training's auc: 0.96103	valid_1's auc: 0.902331
Partial score of fold 2 is: 0.9023313035127103
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.917722	valid_1's auc: 0.890971
[200]	training's auc: 0.930803	valid_1's auc: 0.897052
[300]	training's auc: 0.940596	valid_1's auc: 0.90043
[400]	training's auc: 0.948581	valid_1's auc: 0.90192
[500]	training's auc: 0.954998	valid_1's auc: 0.903129
[600]	training's auc: 0.960751	valid_1's auc: 0.903675
[700]	training's auc: 0.965587	valid_1's auc: 0.904204
[800]	training's auc: 0.969934	valid_1's auc: 0.904306
[900]	training's auc: 0.973566	valid_1's auc: 0.904175
Early stopping, best iteration is:
[804]	training's auc: 0.970092	valid_1's auc: 0.904349
Partial score of fold 3 is: 0.9043488838103488
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.917678	valid_1's auc: 0.889404
[200]	training's auc: 0.930807	valid_1's auc: 0.897689
[300]	training's auc: 0.940619	valid_1's auc: 0.901919
[400]	training's auc: 0.948251	valid_1's auc: 0.904054
[500]	training's auc: 0.955	valid_1's auc: 0.905318
[600]	training's auc: 0.960607	valid_1's auc: 0.90644
[700]	training's auc: 0.965723	valid_1's auc: 0.907052
[800]	training's auc: 0.969917	valid_1's auc: 0.907207
[900]	training's auc: 0.973634	valid_1's auc: 0.907356
[1000]	training's auc: 0.976784	valid_1's auc: 0.907542
[1100]	training's auc: 0.979519	valid_1's auc: 0.907441
Early stopping, best iteration is:
[1044]	training's auc: 0.97801	valid_1's auc: 0.907665
Partial score of fold 4 is: 0.9076650416704095
Our oof AUC score is:  0.9052672026616605
auc:  0.9052672026616605
|  15       |  0.9053   |  0.5689   |  2.251    |  2.164    |  0.01598  |  16.22    |  9.151    |  9.468    |
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.903261	valid_1's auc: 0.888553
[200]	training's auc: 0.910423	valid_1's auc: 0.891666
[300]	training's auc: 0.916288	valid_1's auc: 0.894609
[400]	training's auc: 0.921216	valid_1's auc: 0.896729
[500]	training's auc: 0.925477	valid_1's auc: 0.898764
[600]	training's auc: 0.929271	valid_1's auc: 0.900324
[700]	training's auc: 0.932635	valid_1's auc: 0.901602
[800]	training's auc: 0.935645	valid_1's auc: 0.902739
[900]	training's auc: 0.938496	valid_1's auc: 0.903507
[1000]	training's auc: 0.941194	valid_1's auc: 0.904085
[1100]	training's auc: 0.943751	valid_1's auc: 0.904657
[1200]	training's auc: 0.946141	valid_1's auc: 0.905193
[1300]	training's auc: 0.94853	valid_1's auc: 0.905692
[1400]	training's auc: 0.950737	valid_1's auc: 0.90608
[1500]	training's auc: 0.952918	valid_1's auc: 0.906484
[1600]	training's auc: 0.955004	valid_1's auc: 0.906848
[1700]	training's auc: 0.95694	valid_1's auc: 0.90705
[1800]	training's auc: 0.958811	valid_1's auc: 0.907276
[1900]	training's auc: 0.960548	valid_1's auc: 0.90748
[2000]	training's auc: 0.962166	valid_1's auc: 0.907608
[2100]	training's auc: 0.963742	valid_1's auc: 0.907744
[2200]	training's auc: 0.965268	valid_1's auc: 0.907859
[2300]	training's auc: 0.966716	valid_1's auc: 0.907975
[2400]	training's auc: 0.968127	valid_1's auc: 0.908087
[2500]	training's auc: 0.969494	valid_1's auc: 0.9081
[2600]	training's auc: 0.97078	valid_1's auc: 0.908311
[2700]	training's auc: 0.97198	valid_1's auc: 0.908349
[2800]	training's auc: 0.973128	valid_1's auc: 0.908432
[2900]	training's auc: 0.974239	valid_1's auc: 0.908512
[3000]	training's auc: 0.975317	valid_1's auc: 0.908587
[3100]	training's auc: 0.976372	valid_1's auc: 0.908599
[3200]	training's auc: 0.977333	valid_1's auc: 0.908634
[3300]	training's auc: 0.978237	valid_1's auc: 0.908726
[3400]	training's auc: 0.979146	valid_1's auc: 0.908785
[3500]	training's auc: 0.980012	valid_1's auc: 0.908753
Early stopping, best iteration is:
[3401]	training's auc: 0.979156	valid_1's auc: 0.908787
Partial score of fold 0 is: 0.9087873166113102
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.903946	valid_1's auc: 0.885243
[200]	training's auc: 0.911245	valid_1's auc: 0.889462
[300]	training's auc: 0.91699	valid_1's auc: 0.892316
[400]	training's auc: 0.921977	valid_1's auc: 0.894851
[500]	training's auc: 0.926229	valid_1's auc: 0.897125
[600]	training's auc: 0.929881	valid_1's auc: 0.898786
[700]	training's auc: 0.93318	valid_1's auc: 0.900133
[800]	training's auc: 0.936222	valid_1's auc: 0.901296
[900]	training's auc: 0.939144	valid_1's auc: 0.902175
[1000]	training's auc: 0.941823	valid_1's auc: 0.902901
[1100]	training's auc: 0.944378	valid_1's auc: 0.903497
[1200]	training's auc: 0.946805	valid_1's auc: 0.904042
[1300]	training's auc: 0.94908	valid_1's auc: 0.904541
[1400]	training's auc: 0.951256	valid_1's auc: 0.90496
[1500]	training's auc: 0.953447	valid_1's auc: 0.905147
[1600]	training's auc: 0.955526	valid_1's auc: 0.905393
[1700]	training's auc: 0.957479	valid_1's auc: 0.905668
[1800]	training's auc: 0.959258	valid_1's auc: 0.905861
[1900]	training's auc: 0.961048	valid_1's auc: 0.906135
[2000]	training's auc: 0.962725	valid_1's auc: 0.906353
[2100]	training's auc: 0.964304	valid_1's auc: 0.906525
[2200]	training's auc: 0.965856	valid_1's auc: 0.906763
[2300]	training's auc: 0.967269	valid_1's auc: 0.906772
[2400]	training's auc: 0.968692	valid_1's auc: 0.90686
[2500]	training's auc: 0.970008	valid_1's auc: 0.906933
[2600]	training's auc: 0.971216	valid_1's auc: 0.907036
[2700]	training's auc: 0.972498	valid_1's auc: 0.90706
[2800]	training's auc: 0.973644	valid_1's auc: 0.907049
Early stopping, best iteration is:
[2759]	training's auc: 0.97315	valid_1's auc: 0.907092
Partial score of fold 1 is: 0.907091871118703
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.904339	valid_1's auc: 0.882637
[200]	training's auc: 0.911377	valid_1's auc: 0.886062
[300]	training's auc: 0.917103	valid_1's auc: 0.888933
[400]	training's auc: 0.922175	valid_1's auc: 0.891605
[500]	training's auc: 0.926418	valid_1's auc: 0.893983
[600]	training's auc: 0.930194	valid_1's auc: 0.895763
[700]	training's auc: 0.933566	valid_1's auc: 0.897103
[800]	training's auc: 0.936584	valid_1's auc: 0.898329
[900]	training's auc: 0.939448	valid_1's auc: 0.899206
[1000]	training's auc: 0.942148	valid_1's auc: 0.899937
[1100]	training's auc: 0.944745	valid_1's auc: 0.900517
[1200]	training's auc: 0.947266	valid_1's auc: 0.90097
[1300]	training's auc: 0.949557	valid_1's auc: 0.901442
[1400]	training's auc: 0.951742	valid_1's auc: 0.901775
[1500]	training's auc: 0.953875	valid_1's auc: 0.902017
[1600]	training's auc: 0.955925	valid_1's auc: 0.902304
[1700]	training's auc: 0.957826	valid_1's auc: 0.902542
[1800]	training's auc: 0.95971	valid_1's auc: 0.902763
[1900]	training's auc: 0.961415	valid_1's auc: 0.902981
[2000]	training's auc: 0.963112	valid_1's auc: 0.903104
[2100]	training's auc: 0.964675	valid_1's auc: 0.903234
[2200]	training's auc: 0.96621	valid_1's auc: 0.903401
[2300]	training's auc: 0.967646	valid_1's auc: 0.903497
[2400]	training's auc: 0.968983	valid_1's auc: 0.903602
[2500]	training's auc: 0.970252	valid_1's auc: 0.903682
[2600]	training's auc: 0.971496	valid_1's auc: 0.903742
[2700]	training's auc: 0.972724	valid_1's auc: 0.903826
[2800]	training's auc: 0.973856	valid_1's auc: 0.903949
[2900]	training's auc: 0.974914	valid_1's auc: 0.904069
[3000]	training's auc: 0.975965	valid_1's auc: 0.904116
[3100]	training's auc: 0.976999	valid_1's auc: 0.904034
Early stopping, best iteration is:
[3001]	training's auc: 0.975979	valid_1's auc: 0.90412
Partial score of fold 2 is: 0.9041202619719966
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.90378	valid_1's auc: 0.887384
[200]	training's auc: 0.910931	valid_1's auc: 0.890954
[300]	training's auc: 0.9167	valid_1's auc: 0.89352
[400]	training's auc: 0.921639	valid_1's auc: 0.895879
[500]	training's auc: 0.926092	valid_1's auc: 0.897925
[600]	training's auc: 0.929953	valid_1's auc: 0.899528
[700]	training's auc: 0.933299	valid_1's auc: 0.900542
[800]	training's auc: 0.936413	valid_1's auc: 0.901575
[900]	training's auc: 0.939345	valid_1's auc: 0.902353
[1000]	training's auc: 0.942052	valid_1's auc: 0.902983
[1100]	training's auc: 0.944699	valid_1's auc: 0.903423
[1200]	training's auc: 0.947196	valid_1's auc: 0.903915
[1300]	training's auc: 0.94945	valid_1's auc: 0.904324
[1400]	training's auc: 0.951658	valid_1's auc: 0.904656
[1500]	training's auc: 0.953795	valid_1's auc: 0.904901
[1600]	training's auc: 0.955824	valid_1's auc: 0.905105
[1700]	training's auc: 0.957744	valid_1's auc: 0.90537
[1800]	training's auc: 0.959568	valid_1's auc: 0.905539
[1900]	training's auc: 0.961297	valid_1's auc: 0.905709
[2000]	training's auc: 0.962979	valid_1's auc: 0.905846
[2100]	training's auc: 0.964532	valid_1's auc: 0.90599
[2200]	training's auc: 0.966011	valid_1's auc: 0.906046
[2300]	training's auc: 0.967435	valid_1's auc: 0.906135
[2400]	training's auc: 0.968822	valid_1's auc: 0.906156
Early stopping, best iteration is:
[2347]	training's auc: 0.968086	valid_1's auc: 0.9062
Partial score of fold 3 is: 0.9061996916111904
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.903971	valid_1's auc: 0.884625
[200]	training's auc: 0.911049	valid_1's auc: 0.888119
[300]	training's auc: 0.916762	valid_1's auc: 0.891722
[400]	training's auc: 0.921667	valid_1's auc: 0.894725
[500]	training's auc: 0.925957	valid_1's auc: 0.897742
[600]	training's auc: 0.929701	valid_1's auc: 0.89978
[700]	training's auc: 0.93308	valid_1's auc: 0.901282
[800]	training's auc: 0.936171	valid_1's auc: 0.902531
[900]	training's auc: 0.939023	valid_1's auc: 0.903475
[1000]	training's auc: 0.941683	valid_1's auc: 0.904301
[1100]	training's auc: 0.944283	valid_1's auc: 0.904894
[1200]	training's auc: 0.946706	valid_1's auc: 0.905428
[1300]	training's auc: 0.949011	valid_1's auc: 0.905775
[1400]	training's auc: 0.951233	valid_1's auc: 0.906064
[1500]	training's auc: 0.953355	valid_1's auc: 0.906351
[1600]	training's auc: 0.955408	valid_1's auc: 0.906662
[1700]	training's auc: 0.957367	valid_1's auc: 0.906905
[1800]	training's auc: 0.959257	valid_1's auc: 0.90712
[1900]	training's auc: 0.961001	valid_1's auc: 0.90727
[2000]	training's auc: 0.962709	valid_1's auc: 0.907512
[2100]	training's auc: 0.964283	valid_1's auc: 0.907577
[2200]	training's auc: 0.965776	valid_1's auc: 0.907738
[2300]	training's auc: 0.967193	valid_1's auc: 0.907783
[2400]	training's auc: 0.968605	valid_1's auc: 0.907869
[2500]	training's auc: 0.969962	valid_1's auc: 0.907937
[2600]	training's auc: 0.971237	valid_1's auc: 0.908006
[2700]	training's auc: 0.972482	valid_1's auc: 0.908045
[2800]	training's auc: 0.973621	valid_1's auc: 0.908107
[2900]	training's auc: 0.974728	valid_1's auc: 0.908186
[3000]	training's auc: 0.975764	valid_1's auc: 0.90824
[3100]	training's auc: 0.976771	valid_1's auc: 0.908233
Early stopping, best iteration is:
[3002]	training's auc: 0.975789	valid_1's auc: 0.908241
Partial score of fold 4 is: 0.9082407027920657
Our oof AUC score is:  0.9068186729520137
auc:  0.9068186729520137
|  16       |  0.9068   |  0.5407   |  2.788    |  4.56     |  0.005362 |  15.85    |  4.922    |  4.002    |
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.896821	valid_1's auc: 0.882074
[200]	training's auc: 0.908553	valid_1's auc: 0.888484
[300]	training's auc: 0.917863	valid_1's auc: 0.893882
[400]	training's auc: 0.925307	valid_1's auc: 0.897655
[500]	training's auc: 0.931052	valid_1's auc: 0.899842
[600]	training's auc: 0.93596	valid_1's auc: 0.901397
[700]	training's auc: 0.940116	valid_1's auc: 0.902309
[800]	training's auc: 0.944275	valid_1's auc: 0.903219
[900]	training's auc: 0.94787	valid_1's auc: 0.903847
[1000]	training's auc: 0.951183	valid_1's auc: 0.904359
[1100]	training's auc: 0.953994	valid_1's auc: 0.904936
[1200]	training's auc: 0.957023	valid_1's auc: 0.905374
[1300]	training's auc: 0.959608	valid_1's auc: 0.905702
[1400]	training's auc: 0.96202	valid_1's auc: 0.905766
[1500]	training's auc: 0.964505	valid_1's auc: 0.906181
[1600]	training's auc: 0.966646	valid_1's auc: 0.906664
Early stopping, best iteration is:
[1595]	training's auc: 0.966539	valid_1's auc: 0.906679
Partial score of fold 0 is: 0.9066791099344617
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.898747	valid_1's auc: 0.879592
[200]	training's auc: 0.909223	valid_1's auc: 0.885734
[300]	training's auc: 0.918445	valid_1's auc: 0.891158
[400]	training's auc: 0.926167	valid_1's auc: 0.895577
[500]	training's auc: 0.931949	valid_1's auc: 0.898111
[600]	training's auc: 0.936902	valid_1's auc: 0.899633
[700]	training's auc: 0.940994	valid_1's auc: 0.900939
[800]	training's auc: 0.944826	valid_1's auc: 0.901634
[900]	training's auc: 0.948483	valid_1's auc: 0.90226
[1000]	training's auc: 0.951818	valid_1's auc: 0.902832
[1100]	training's auc: 0.95479	valid_1's auc: 0.903117
[1200]	training's auc: 0.957655	valid_1's auc: 0.903501
[1300]	training's auc: 0.960165	valid_1's auc: 0.903553
[1400]	training's auc: 0.962484	valid_1's auc: 0.90365
[1500]	training's auc: 0.964844	valid_1's auc: 0.903774
[1600]	training's auc: 0.967003	valid_1's auc: 0.903949
[1700]	training's auc: 0.968982	valid_1's auc: 0.904151
[1800]	training's auc: 0.970821	valid_1's auc: 0.904225
[1900]	training's auc: 0.972419	valid_1's auc: 0.904319
[2000]	training's auc: 0.974004	valid_1's auc: 0.904464
[2100]	training's auc: 0.975508	valid_1's auc: 0.904478
Early stopping, best iteration is:
[2087]	training's auc: 0.975331	valid_1's auc: 0.9045
Partial score of fold 1 is: 0.9045001183517595
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.897911	valid_1's auc: 0.87725
[200]	training's auc: 0.910329	valid_1's auc: 0.884487
[300]	training's auc: 0.918799	valid_1's auc: 0.889449
[400]	training's auc: 0.926209	valid_1's auc: 0.893075
[500]	training's auc: 0.931878	valid_1's auc: 0.895206
[600]	training's auc: 0.936695	valid_1's auc: 0.896344
[700]	training's auc: 0.941036	valid_1's auc: 0.897175
[800]	training's auc: 0.945032	valid_1's auc: 0.89807
[900]	training's auc: 0.948376	valid_1's auc: 0.898594
[1000]	training's auc: 0.951767	valid_1's auc: 0.899075
[1100]	training's auc: 0.954834	valid_1's auc: 0.899375
[1200]	training's auc: 0.957656	valid_1's auc: 0.899707
[1300]	training's auc: 0.960191	valid_1's auc: 0.899748
[1400]	training's auc: 0.962734	valid_1's auc: 0.900012
[1500]	training's auc: 0.964821	valid_1's auc: 0.900164
[1600]	training's auc: 0.966976	valid_1's auc: 0.90025
[1700]	training's auc: 0.969042	valid_1's auc: 0.900448
[1800]	training's auc: 0.970975	valid_1's auc: 0.90051
[1900]	training's auc: 0.97259	valid_1's auc: 0.900481
Early stopping, best iteration is:
[1857]	training's auc: 0.971925	valid_1's auc: 0.900612
Partial score of fold 2 is: 0.9006124515097012
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.897742	valid_1's auc: 0.884525
[200]	training's auc: 0.909431	valid_1's auc: 0.88996
[300]	training's auc: 0.918904	valid_1's auc: 0.894203
[400]	training's auc: 0.926302	valid_1's auc: 0.897309
[500]	training's auc: 0.932089	valid_1's auc: 0.89943
[600]	training's auc: 0.936753	valid_1's auc: 0.900774
[700]	training's auc: 0.940974	valid_1's auc: 0.901735
[800]	training's auc: 0.94494	valid_1's auc: 0.902193
[900]	training's auc: 0.94858	valid_1's auc: 0.902799
[1000]	training's auc: 0.951817	valid_1's auc: 0.903362
[1100]	training's auc: 0.954751	valid_1's auc: 0.903587
[1200]	training's auc: 0.957757	valid_1's auc: 0.903746
[1300]	training's auc: 0.960525	valid_1's auc: 0.903818
[1400]	training's auc: 0.962801	valid_1's auc: 0.90383
Early stopping, best iteration is:
[1332]	training's auc: 0.961196	valid_1's auc: 0.903995
Partial score of fold 3 is: 0.9039949758564966
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.897243	valid_1's auc: 0.880857
[200]	training's auc: 0.908782	valid_1's auc: 0.886934
[300]	training's auc: 0.918149	valid_1's auc: 0.892552
[400]	training's auc: 0.925307	valid_1's auc: 0.896549
[500]	training's auc: 0.931264	valid_1's auc: 0.899202
[600]	training's auc: 0.936075	valid_1's auc: 0.900545
[700]	training's auc: 0.940408	valid_1's auc: 0.901541
[800]	training's auc: 0.944303	valid_1's auc: 0.902362
[900]	training's auc: 0.947724	valid_1's auc: 0.903073
[1000]	training's auc: 0.95119	valid_1's auc: 0.903755
[1100]	training's auc: 0.954207	valid_1's auc: 0.904296
[1200]	training's auc: 0.957025	valid_1's auc: 0.904871
[1300]	training's auc: 0.959626	valid_1's auc: 0.905081
[1400]	training's auc: 0.962086	valid_1's auc: 0.905259
Early stopping, best iteration is:
[1359]	training's auc: 0.961056	valid_1's auc: 0.905285
Partial score of fold 4 is: 0.9052850870108845
Our oof AUC score is:  0.9040954500623035
auc:  0.9040954500623035
|  17       |  0.9041   |  0.9537   |  4.718    |  4.643    |  0.009437 |  16.53    |  1.003    |  9.899    |
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.902582	valid_1's auc: 0.885262
[200]	training's auc: 0.918121	valid_1's auc: 0.893365
[300]	training's auc: 0.928334	valid_1's auc: 0.898278
[400]	training's auc: 0.93595	valid_1's auc: 0.901148
[500]	training's auc: 0.942439	valid_1's auc: 0.902767
[600]	training's auc: 0.947923	valid_1's auc: 0.904273
[700]	training's auc: 0.953029	valid_1's auc: 0.905016
[800]	training's auc: 0.95747	valid_1's auc: 0.905439
[900]	training's auc: 0.961444	valid_1's auc: 0.905914
[1000]	training's auc: 0.96501	valid_1's auc: 0.906279
[1100]	training's auc: 0.968227	valid_1's auc: 0.906557
[1200]	training's auc: 0.971367	valid_1's auc: 0.906765
[1300]	training's auc: 0.973933	valid_1's auc: 0.906857
[1400]	training's auc: 0.976334	valid_1's auc: 0.90682
[1500]	training's auc: 0.978708	valid_1's auc: 0.907022
[1600]	training's auc: 0.980652	valid_1's auc: 0.907095
[1700]	training's auc: 0.982426	valid_1's auc: 0.907061
Early stopping, best iteration is:
[1665]	training's auc: 0.981797	valid_1's auc: 0.907153
Partial score of fold 0 is: 0.9071530446555511
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.903434	valid_1's auc: 0.883542
[200]	training's auc: 0.917434	valid_1's auc: 0.890742
[300]	training's auc: 0.928114	valid_1's auc: 0.896009
[400]	training's auc: 0.935906	valid_1's auc: 0.899238
[500]	training's auc: 0.942207	valid_1's auc: 0.901319
[600]	training's auc: 0.947954	valid_1's auc: 0.902299
[700]	training's auc: 0.952894	valid_1's auc: 0.903014
[800]	training's auc: 0.957452	valid_1's auc: 0.903668
[900]	training's auc: 0.96179	valid_1's auc: 0.903836
[1000]	training's auc: 0.965454	valid_1's auc: 0.904068
[1100]	training's auc: 0.968818	valid_1's auc: 0.904204
[1200]	training's auc: 0.971722	valid_1's auc: 0.90432
[1300]	training's auc: 0.974281	valid_1's auc: 0.904411
[1400]	training's auc: 0.976605	valid_1's auc: 0.904418
Early stopping, best iteration is:
[1370]	training's auc: 0.975818	valid_1's auc: 0.904523
Partial score of fold 1 is: 0.9045230725624438
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.903434	valid_1's auc: 0.880485
[200]	training's auc: 0.918375	valid_1's auc: 0.88848
[300]	training's auc: 0.928455	valid_1's auc: 0.89342
[400]	training's auc: 0.936243	valid_1's auc: 0.896031
[500]	training's auc: 0.942787	valid_1's auc: 0.897675
[600]	training's auc: 0.948173	valid_1's auc: 0.898625
[700]	training's auc: 0.953205	valid_1's auc: 0.899186
[800]	training's auc: 0.957661	valid_1's auc: 0.900022
[900]	training's auc: 0.961579	valid_1's auc: 0.900327
[1000]	training's auc: 0.965375	valid_1's auc: 0.900553
[1100]	training's auc: 0.968744	valid_1's auc: 0.900481
Early stopping, best iteration is:
[1028]	training's auc: 0.966481	valid_1's auc: 0.900708
Partial score of fold 2 is: 0.900707622908679
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.903735	valid_1's auc: 0.887609
[200]	training's auc: 0.918061	valid_1's auc: 0.89374
[300]	training's auc: 0.928823	valid_1's auc: 0.897997
[400]	training's auc: 0.936467	valid_1's auc: 0.900204
[500]	training's auc: 0.942992	valid_1's auc: 0.901683
[600]	training's auc: 0.948491	valid_1's auc: 0.902755
[700]	training's auc: 0.953492	valid_1's auc: 0.903333
[800]	training's auc: 0.958113	valid_1's auc: 0.903788
[900]	training's auc: 0.962166	valid_1's auc: 0.903943
Early stopping, best iteration is:
[839]	training's auc: 0.959621	valid_1's auc: 0.903971
Partial score of fold 3 is: 0.9039705125204597
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.902604	valid_1's auc: 0.883563
[200]	training's auc: 0.917194	valid_1's auc: 0.890796
[300]	training's auc: 0.92786	valid_1's auc: 0.897038
[400]	training's auc: 0.935375	valid_1's auc: 0.900036
[500]	training's auc: 0.942076	valid_1's auc: 0.902109
[600]	training's auc: 0.947591	valid_1's auc: 0.903099
[700]	training's auc: 0.952814	valid_1's auc: 0.904068
[800]	training's auc: 0.957556	valid_1's auc: 0.904817
[900]	training's auc: 0.961486	valid_1's auc: 0.90512
[1000]	training's auc: 0.965139	valid_1's auc: 0.905741
[1100]	training's auc: 0.968301	valid_1's auc: 0.905859
[1200]	training's auc: 0.971116	valid_1's auc: 0.905982
[1300]	training's auc: 0.97368	valid_1's auc: 0.906052
[1400]	training's auc: 0.976165	valid_1's auc: 0.906281
Early stopping, best iteration is:
[1388]	training's auc: 0.975839	valid_1's auc: 0.906301
Partial score of fold 4 is: 0.9063010127934201
Our oof AUC score is:  0.9044652470760501
auc:  0.9044652470760501
|  18       |  0.9045   |  0.944    |  0.1648   |  4.82     |  0.01237  |  15.05    |  1.057    |  9.867    |
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.906448	valid_1's auc: 0.886464
[200]	training's auc: 0.923145	valid_1's auc: 0.895505
[300]	training's auc: 0.933518	valid_1's auc: 0.899788
[400]	training's auc: 0.941298	valid_1's auc: 0.902417
[500]	training's auc: 0.948279	valid_1's auc: 0.903647
[600]	training's auc: 0.954057	valid_1's auc: 0.904576
[700]	training's auc: 0.959568	valid_1's auc: 0.905105
[800]	training's auc: 0.964035	valid_1's auc: 0.90548
[900]	training's auc: 0.967814	valid_1's auc: 0.905862
[1000]	training's auc: 0.971481	valid_1's auc: 0.906131
[1100]	training's auc: 0.97459	valid_1's auc: 0.906307
[1200]	training's auc: 0.977681	valid_1's auc: 0.906287
Early stopping, best iteration is:
[1129]	training's auc: 0.975601	valid_1's auc: 0.906357
Partial score of fold 0 is: 0.9063566956188741
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.90616	valid_1's auc: 0.88428
[200]	training's auc: 0.922136	valid_1's auc: 0.892576
[300]	training's auc: 0.93347	valid_1's auc: 0.897361
[400]	training's auc: 0.94145	valid_1's auc: 0.900255
[500]	training's auc: 0.948236	valid_1's auc: 0.901485
[600]	training's auc: 0.954227	valid_1's auc: 0.902371
[700]	training's auc: 0.959427	valid_1's auc: 0.903125
[800]	training's auc: 0.964178	valid_1's auc: 0.903509
[900]	training's auc: 0.968409	valid_1's auc: 0.90379
[1000]	training's auc: 0.97191	valid_1's auc: 0.903668
Early stopping, best iteration is:
[966]	training's auc: 0.97082	valid_1's auc: 0.903874
Partial score of fold 1 is: 0.9038743240003798
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.90719	valid_1's auc: 0.882868
[200]	training's auc: 0.923336	valid_1's auc: 0.891142
[300]	training's auc: 0.933941	valid_1's auc: 0.895568
[400]	training's auc: 0.942033	valid_1's auc: 0.897283
[500]	training's auc: 0.948895	valid_1's auc: 0.898747
[600]	training's auc: 0.954312	valid_1's auc: 0.899181
[700]	training's auc: 0.95976	valid_1's auc: 0.899674
[800]	training's auc: 0.964449	valid_1's auc: 0.900358
[900]	training's auc: 0.968166	valid_1's auc: 0.900643
[1000]	training's auc: 0.971859	valid_1's auc: 0.900769
[1100]	training's auc: 0.975232	valid_1's auc: 0.900711
Early stopping, best iteration is:
[1035]	training's auc: 0.973139	valid_1's auc: 0.901014
Partial score of fold 2 is: 0.9010141690424965
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.907648	valid_1's auc: 0.888997
[200]	training's auc: 0.92319	valid_1's auc: 0.89586
[300]	training's auc: 0.93392	valid_1's auc: 0.89945
[400]	training's auc: 0.94217	valid_1's auc: 0.901067
[500]	training's auc: 0.948881	valid_1's auc: 0.902202
[600]	training's auc: 0.954591	valid_1's auc: 0.903013
[700]	training's auc: 0.959883	valid_1's auc: 0.903261
[800]	training's auc: 0.964452	valid_1's auc: 0.903588
[900]	training's auc: 0.96856	valid_1's auc: 0.903523
Early stopping, best iteration is:
[837]	training's auc: 0.965926	valid_1's auc: 0.903697
Partial score of fold 3 is: 0.9036967794753137
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.90539	valid_1's auc: 0.88489
[200]	training's auc: 0.922125	valid_1's auc: 0.893792
[300]	training's auc: 0.933256	valid_1's auc: 0.898771
[400]	training's auc: 0.94122	valid_1's auc: 0.901
[500]	training's auc: 0.948247	valid_1's auc: 0.902872
[600]	training's auc: 0.953883	valid_1's auc: 0.904011
[700]	training's auc: 0.959631	valid_1's auc: 0.90471
[800]	training's auc: 0.964319	valid_1's auc: 0.905099
[900]	training's auc: 0.968407	valid_1's auc: 0.905139
[1000]	training's auc: 0.971982	valid_1's auc: 0.905762
Early stopping, best iteration is:
[998]	training's auc: 0.971911	valid_1's auc: 0.905787
Partial score of fold 4 is: 0.9057865288588018
Our oof AUC score is:  0.9041121222278096
auc:  0.9041121222278096
|  19       |  0.9041   |  0.9797   |  0.3888   |  4.806    |  0.01515  |  13.54    |  1.038    |  9.881    |
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.898044	valid_1's auc: 0.884509
[200]	training's auc: 0.908465	valid_1's auc: 0.890167
[300]	training's auc: 0.916766	valid_1's auc: 0.894674
[400]	training's auc: 0.923793	valid_1's auc: 0.898201
[500]	training's auc: 0.929323	valid_1's auc: 0.900608
[600]	training's auc: 0.933965	valid_1's auc: 0.902232
[700]	training's auc: 0.938204	valid_1's auc: 0.903406
[800]	training's auc: 0.942007	valid_1's auc: 0.904392
[900]	training's auc: 0.945541	valid_1's auc: 0.905047
[1000]	training's auc: 0.94889	valid_1's auc: 0.905717
[1100]	training's auc: 0.952083	valid_1's auc: 0.906238
[1200]	training's auc: 0.954966	valid_1's auc: 0.906735
[1300]	training's auc: 0.957687	valid_1's auc: 0.907159
[1400]	training's auc: 0.960208	valid_1's auc: 0.907462
[1500]	training's auc: 0.962645	valid_1's auc: 0.907757
[1600]	training's auc: 0.964862	valid_1's auc: 0.908063
[1700]	training's auc: 0.967005	valid_1's auc: 0.908355
[1800]	training's auc: 0.969017	valid_1's auc: 0.908492
[1900]	training's auc: 0.97089	valid_1's auc: 0.908648
[2000]	training's auc: 0.972702	valid_1's auc: 0.908762
[2100]	training's auc: 0.974327	valid_1's auc: 0.908809
[2200]	training's auc: 0.975923	valid_1's auc: 0.908863
[2300]	training's auc: 0.977406	valid_1's auc: 0.90902
[2400]	training's auc: 0.978841	valid_1's auc: 0.909011
[2500]	training's auc: 0.980143	valid_1's auc: 0.909104
[2600]	training's auc: 0.981375	valid_1's auc: 0.909002
Early stopping, best iteration is:
[2506]	training's auc: 0.980213	valid_1's auc: 0.909112
Partial score of fold 0 is: 0.9091122939586326
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.898713	valid_1's auc: 0.88178
[200]	training's auc: 0.908821	valid_1's auc: 0.88797
[300]	training's auc: 0.917053	valid_1's auc: 0.892579
[400]	training's auc: 0.924089	valid_1's auc: 0.896227
[500]	training's auc: 0.9297	valid_1's auc: 0.899002
[600]	training's auc: 0.93442	valid_1's auc: 0.900701
[700]	training's auc: 0.938534	valid_1's auc: 0.901971
[800]	training's auc: 0.942305	valid_1's auc: 0.902988
[900]	training's auc: 0.945969	valid_1's auc: 0.903782
[1000]	training's auc: 0.949271	valid_1's auc: 0.904346
[1100]	training's auc: 0.952356	valid_1's auc: 0.904756
[1200]	training's auc: 0.955393	valid_1's auc: 0.905241
[1300]	training's auc: 0.958118	valid_1's auc: 0.905513
[1400]	training's auc: 0.960674	valid_1's auc: 0.905902
[1500]	training's auc: 0.963153	valid_1's auc: 0.906265
[1600]	training's auc: 0.965324	valid_1's auc: 0.906361
[1700]	training's auc: 0.967425	valid_1's auc: 0.906516
[1800]	training's auc: 0.969393	valid_1's auc: 0.906663
[1900]	training's auc: 0.971286	valid_1's auc: 0.906721
[2000]	training's auc: 0.973035	valid_1's auc: 0.906833
[2100]	training's auc: 0.974656	valid_1's auc: 0.906829
[2200]	training's auc: 0.976212	valid_1's auc: 0.906983
[2300]	training's auc: 0.977711	valid_1's auc: 0.907027
[2400]	training's auc: 0.979108	valid_1's auc: 0.90705
[2500]	training's auc: 0.980441	valid_1's auc: 0.907103
[2600]	training's auc: 0.981619	valid_1's auc: 0.907157
[2700]	training's auc: 0.982788	valid_1's auc: 0.907111
Early stopping, best iteration is:
[2603]	training's auc: 0.981649	valid_1's auc: 0.907158
Partial score of fold 1 is: 0.9071577184193029
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.899689	valid_1's auc: 0.879914
[200]	training's auc: 0.909613	valid_1's auc: 0.885548
[300]	training's auc: 0.917366	valid_1's auc: 0.88975
[400]	training's auc: 0.924227	valid_1's auc: 0.893392
[500]	training's auc: 0.929831	valid_1's auc: 0.896113
[600]	training's auc: 0.934634	valid_1's auc: 0.897755
[700]	training's auc: 0.938811	valid_1's auc: 0.898812
[800]	training's auc: 0.942514	valid_1's auc: 0.89958
[900]	training's auc: 0.946043	valid_1's auc: 0.900227
[1000]	training's auc: 0.949275	valid_1's auc: 0.900761
[1100]	training's auc: 0.952487	valid_1's auc: 0.901112
[1200]	training's auc: 0.955423	valid_1's auc: 0.901348
[1300]	training's auc: 0.958237	valid_1's auc: 0.901573
[1400]	training's auc: 0.960732	valid_1's auc: 0.901852
[1500]	training's auc: 0.96317	valid_1's auc: 0.902021
[1600]	training's auc: 0.965422	valid_1's auc: 0.902134
[1700]	training's auc: 0.967608	valid_1's auc: 0.902336
[1800]	training's auc: 0.969503	valid_1's auc: 0.902507
[1900]	training's auc: 0.971367	valid_1's auc: 0.902662
[2000]	training's auc: 0.97313	valid_1's auc: 0.902779
Early stopping, best iteration is:
[1968]	training's auc: 0.972613	valid_1's auc: 0.902804
Partial score of fold 2 is: 0.9028042582510776
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.89803	valid_1's auc: 0.886823
[200]	training's auc: 0.908199	valid_1's auc: 0.891599
[300]	training's auc: 0.916503	valid_1's auc: 0.895397
[400]	training's auc: 0.923719	valid_1's auc: 0.898384
[500]	training's auc: 0.929353	valid_1's auc: 0.900259
[600]	training's auc: 0.934242	valid_1's auc: 0.901609
[700]	training's auc: 0.938386	valid_1's auc: 0.902765
[800]	training's auc: 0.942177	valid_1's auc: 0.90354
[900]	training's auc: 0.945819	valid_1's auc: 0.904056
[1000]	training's auc: 0.949123	valid_1's auc: 0.904532
[1100]	training's auc: 0.952416	valid_1's auc: 0.904951
[1200]	training's auc: 0.955462	valid_1's auc: 0.905184
[1300]	training's auc: 0.958208	valid_1's auc: 0.905394
[1400]	training's auc: 0.960755	valid_1's auc: 0.905495
[1500]	training's auc: 0.963164	valid_1's auc: 0.905687
[1600]	training's auc: 0.965464	valid_1's auc: 0.905776
[1700]	training's auc: 0.967583	valid_1's auc: 0.905944
[1800]	training's auc: 0.969568	valid_1's auc: 0.906058
[1900]	training's auc: 0.97148	valid_1's auc: 0.906202
[2000]	training's auc: 0.973188	valid_1's auc: 0.906312
[2100]	training's auc: 0.974905	valid_1's auc: 0.906387
[2200]	training's auc: 0.976439	valid_1's auc: 0.906524
[2300]	training's auc: 0.977843	valid_1's auc: 0.906555
[2400]	training's auc: 0.979187	valid_1's auc: 0.906542
Early stopping, best iteration is:
[2311]	training's auc: 0.977993	valid_1's auc: 0.906573
Partial score of fold 3 is: 0.906573388858447
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.898478	valid_1's auc: 0.882974
[200]	training's auc: 0.908317	valid_1's auc: 0.888471
[300]	training's auc: 0.916413	valid_1's auc: 0.893061
[400]	training's auc: 0.923443	valid_1's auc: 0.897019
[500]	training's auc: 0.929168	valid_1's auc: 0.899841
[600]	training's auc: 0.933965	valid_1's auc: 0.901589
[700]	training's auc: 0.938173	valid_1's auc: 0.902681
[800]	training's auc: 0.94204	valid_1's auc: 0.903668
[900]	training's auc: 0.945674	valid_1's auc: 0.904484
[1000]	training's auc: 0.94904	valid_1's auc: 0.905084
[1100]	training's auc: 0.952175	valid_1's auc: 0.905568
[1200]	training's auc: 0.955155	valid_1's auc: 0.906138
[1300]	training's auc: 0.957939	valid_1's auc: 0.906429
[1400]	training's auc: 0.960469	valid_1's auc: 0.906773
[1500]	training's auc: 0.962792	valid_1's auc: 0.907185
[1600]	training's auc: 0.965202	valid_1's auc: 0.907293
[1700]	training's auc: 0.96731	valid_1's auc: 0.907445
[1800]	training's auc: 0.969341	valid_1's auc: 0.907643
[1900]	training's auc: 0.971219	valid_1's auc: 0.907791
[2000]	training's auc: 0.972932	valid_1's auc: 0.907824
[2100]	training's auc: 0.974582	valid_1's auc: 0.907903
[2200]	training's auc: 0.976126	valid_1's auc: 0.908016
[2300]	training's auc: 0.977642	valid_1's auc: 0.908018
[2400]	training's auc: 0.978975	valid_1's auc: 0.908123
[2500]	training's auc: 0.98029	valid_1's auc: 0.908202
Early stopping, best iteration is:
[2490]	training's auc: 0.98016	valid_1's auc: 0.908236
Partial score of fold 4 is: 0.9082357271982955
Our oof AUC score is:  0.9067586694393703
auc:  0.9067586694393703
|  20       |  0.9068   |  0.7743   |  0.8008   |  4.974    |  0.008075 |  16.98    |  1.112    |  1.128    |
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.909006	valid_1's auc: 0.891416
[200]	training's auc: 0.922713	valid_1's auc: 0.898592
[300]	training's auc: 0.932601	valid_1's auc: 0.902501
[400]	training's auc: 0.940376	valid_1's auc: 0.904835
[500]	training's auc: 0.947121	valid_1's auc: 0.906158
[600]	training's auc: 0.953181	valid_1's auc: 0.907302
[700]	training's auc: 0.958555	valid_1's auc: 0.907784
[800]	training's auc: 0.963175	valid_1's auc: 0.90807
[900]	training's auc: 0.96748	valid_1's auc: 0.908389
[1000]	training's auc: 0.971051	valid_1's auc: 0.908474
[1100]	training's auc: 0.974251	valid_1's auc: 0.908679
[1200]	training's auc: 0.97714	valid_1's auc: 0.908725
Early stopping, best iteration is:
[1163]	training's auc: 0.976116	valid_1's auc: 0.908801
Partial score of fold 0 is: 0.9088013755942088
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.909217	valid_1's auc: 0.888755
[200]	training's auc: 0.92272	valid_1's auc: 0.896414
[300]	training's auc: 0.932767	valid_1's auc: 0.900648
[400]	training's auc: 0.940562	valid_1's auc: 0.903113
[500]	training's auc: 0.947598	valid_1's auc: 0.904412
[600]	training's auc: 0.953722	valid_1's auc: 0.905484
[700]	training's auc: 0.959031	valid_1's auc: 0.9061
[800]	training's auc: 0.963678	valid_1's auc: 0.906555
[900]	training's auc: 0.96784	valid_1's auc: 0.906786
[1000]	training's auc: 0.971486	valid_1's auc: 0.907022
[1100]	training's auc: 0.974792	valid_1's auc: 0.907321
[1200]	training's auc: 0.977714	valid_1's auc: 0.907294
Early stopping, best iteration is:
[1122]	training's auc: 0.97543	valid_1's auc: 0.907406
Partial score of fold 1 is: 0.907406106347725
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.909478	valid_1's auc: 0.886307
[200]	training's auc: 0.923581	valid_1's auc: 0.893391
[300]	training's auc: 0.933384	valid_1's auc: 0.897576
[400]	training's auc: 0.941188	valid_1's auc: 0.899598
[500]	training's auc: 0.947949	valid_1's auc: 0.900883
[600]	training's auc: 0.954009	valid_1's auc: 0.901714
[700]	training's auc: 0.959245	valid_1's auc: 0.902053
[800]	training's auc: 0.9638	valid_1's auc: 0.902429
[900]	training's auc: 0.967862	valid_1's auc: 0.902798
[1000]	training's auc: 0.971362	valid_1's auc: 0.903009
[1100]	training's auc: 0.974603	valid_1's auc: 0.903065
[1200]	training's auc: 0.977649	valid_1's auc: 0.903238
[1300]	training's auc: 0.980266	valid_1's auc: 0.903171
Early stopping, best iteration is:
[1274]	training's auc: 0.979602	valid_1's auc: 0.90334
Partial score of fold 2 is: 0.9033396680421605
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.909413	valid_1's auc: 0.8917
[200]	training's auc: 0.923134	valid_1's auc: 0.898187
[300]	training's auc: 0.933117	valid_1's auc: 0.90176
[400]	training's auc: 0.941075	valid_1's auc: 0.903569
[500]	training's auc: 0.947696	valid_1's auc: 0.904794
[600]	training's auc: 0.95394	valid_1's auc: 0.905547
[700]	training's auc: 0.959209	valid_1's auc: 0.906086
[800]	training's auc: 0.963998	valid_1's auc: 0.906602
[900]	training's auc: 0.968265	valid_1's auc: 0.906995
[1000]	training's auc: 0.971692	valid_1's auc: 0.907059
[1100]	training's auc: 0.974969	valid_1's auc: 0.907099
[1200]	training's auc: 0.977856	valid_1's auc: 0.907119
[1300]	training's auc: 0.980397	valid_1's auc: 0.907242
[1400]	training's auc: 0.982738	valid_1's auc: 0.907166
Early stopping, best iteration is:
[1301]	training's auc: 0.980419	valid_1's auc: 0.907262
Partial score of fold 3 is: 0.9072618677994536
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.908932	valid_1's auc: 0.890047
[200]	training's auc: 0.922511	valid_1's auc: 0.897589
[300]	training's auc: 0.932516	valid_1's auc: 0.90208
[400]	training's auc: 0.940385	valid_1's auc: 0.90432
[500]	training's auc: 0.947212	valid_1's auc: 0.905792
[600]	training's auc: 0.953521	valid_1's auc: 0.906744
[700]	training's auc: 0.958798	valid_1's auc: 0.907353
[800]	training's auc: 0.963263	valid_1's auc: 0.907807
[900]	training's auc: 0.967532	valid_1's auc: 0.907922
[1000]	training's auc: 0.971149	valid_1's auc: 0.908282
[1100]	training's auc: 0.974469	valid_1's auc: 0.908546
[1200]	training's auc: 0.977404	valid_1's auc: 0.908812
[1300]	training's auc: 0.980014	valid_1's auc: 0.908731
Early stopping, best iteration is:
[1218]	training's auc: 0.977901	valid_1's auc: 0.908868
Partial score of fold 4 is: 0.9088684945666329
Our oof AUC score is:  0.9070693178073428
auc:  0.9070693178073428
|  21       |  0.9071   |  0.6774   |  0.02145  |  4.984    |  0.01536  |  16.94    |  1.246    |  1.34     |
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.904118	valid_1's auc: 0.889589
[200]	training's auc: 0.916242	valid_1's auc: 0.896039
[300]	training's auc: 0.925455	valid_1's auc: 0.900812
[400]	training's auc: 0.932609	valid_1's auc: 0.903701
[500]	training's auc: 0.938686	valid_1's auc: 0.905264
[600]	training's auc: 0.944157	valid_1's auc: 0.906543
[700]	training's auc: 0.949018	valid_1's auc: 0.907535
[800]	training's auc: 0.953357	valid_1's auc: 0.908213
[900]	training's auc: 0.957564	valid_1's auc: 0.908497
Early stopping, best iteration is:
[883]	training's auc: 0.956952	valid_1's auc: 0.908535
Partial score of fold 0 is: 0.9085348956770701
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.904615	valid_1's auc: 0.887826
[200]	training's auc: 0.91649	valid_1's auc: 0.894051
[300]	training's auc: 0.925866	valid_1's auc: 0.89888
[400]	training's auc: 0.933084	valid_1's auc: 0.901573
[500]	training's auc: 0.939162	valid_1's auc: 0.903391
[600]	training's auc: 0.944648	valid_1's auc: 0.904527
[700]	training's auc: 0.949518	valid_1's auc: 0.905312
[800]	training's auc: 0.953883	valid_1's auc: 0.905926
[900]	training's auc: 0.958027	valid_1's auc: 0.906465
[1000]	training's auc: 0.961784	valid_1's auc: 0.906937
[1100]	training's auc: 0.965182	valid_1's auc: 0.907168
[1200]	training's auc: 0.96822	valid_1's auc: 0.907392
[1300]	training's auc: 0.970984	valid_1's auc: 0.907509
[1400]	training's auc: 0.973641	valid_1's auc: 0.907622
[1500]	training's auc: 0.976032	valid_1's auc: 0.907702
Early stopping, best iteration is:
[1466]	training's auc: 0.975264	valid_1's auc: 0.907716
Partial score of fold 1 is: 0.9077163839542153
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.905612	valid_1's auc: 0.885085
[200]	training's auc: 0.916921	valid_1's auc: 0.891274
[300]	training's auc: 0.926082	valid_1's auc: 0.895897
[400]	training's auc: 0.933256	valid_1's auc: 0.898529
[500]	training's auc: 0.939419	valid_1's auc: 0.900124
[600]	training's auc: 0.944975	valid_1's auc: 0.900999
[700]	training's auc: 0.94982	valid_1's auc: 0.901722
[800]	training's auc: 0.954102	valid_1's auc: 0.902248
[900]	training's auc: 0.958112	valid_1's auc: 0.902728
[1000]	training's auc: 0.96168	valid_1's auc: 0.903206
[1100]	training's auc: 0.965153	valid_1's auc: 0.90343
[1200]	training's auc: 0.968308	valid_1's auc: 0.903655
[1300]	training's auc: 0.971163	valid_1's auc: 0.903786
[1400]	training's auc: 0.973648	valid_1's auc: 0.904007
[1500]	training's auc: 0.976081	valid_1's auc: 0.904146
[1600]	training's auc: 0.978219	valid_1's auc: 0.904347
[1700]	training's auc: 0.980273	valid_1's auc: 0.90442
[1800]	training's auc: 0.98213	valid_1's auc: 0.904458
[1900]	training's auc: 0.983743	valid_1's auc: 0.904619
[2000]	training's auc: 0.985336	valid_1's auc: 0.904568
Early stopping, best iteration is:
[1927]	training's auc: 0.984187	valid_1's auc: 0.904628
Partial score of fold 2 is: 0.9046283830134316
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.904876	valid_1's auc: 0.890882
[200]	training's auc: 0.916394	valid_1's auc: 0.896613
[300]	training's auc: 0.925952	valid_1's auc: 0.900577
[400]	training's auc: 0.933278	valid_1's auc: 0.903102
[500]	training's auc: 0.939244	valid_1's auc: 0.904334
[600]	training's auc: 0.944756	valid_1's auc: 0.905297
[700]	training's auc: 0.949626	valid_1's auc: 0.906067
[800]	training's auc: 0.954111	valid_1's auc: 0.906472
[900]	training's auc: 0.958309	valid_1's auc: 0.90681
[1000]	training's auc: 0.961902	valid_1's auc: 0.90703
[1100]	training's auc: 0.965182	valid_1's auc: 0.907267
[1200]	training's auc: 0.968365	valid_1's auc: 0.907255
[1300]	training's auc: 0.971217	valid_1's auc: 0.90743
[1400]	training's auc: 0.97396	valid_1's auc: 0.907516
Early stopping, best iteration is:
[1354]	training's auc: 0.972755	valid_1's auc: 0.907557
Partial score of fold 3 is: 0.9075568978936916
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.904494	valid_1's auc: 0.889127
[200]	training's auc: 0.915953	valid_1's auc: 0.895406
[300]	training's auc: 0.925297	valid_1's auc: 0.900556
[400]	training's auc: 0.932654	valid_1's auc: 0.903255
[500]	training's auc: 0.938792	valid_1's auc: 0.905073
[600]	training's auc: 0.944236	valid_1's auc: 0.906523
[700]	training's auc: 0.949195	valid_1's auc: 0.907354
[800]	training's auc: 0.953571	valid_1's auc: 0.907979
[900]	training's auc: 0.957809	valid_1's auc: 0.908546
[1000]	training's auc: 0.961577	valid_1's auc: 0.90896
[1100]	training's auc: 0.964969	valid_1's auc: 0.908969
Early stopping, best iteration is:
[1009]	training's auc: 0.961846	valid_1's auc: 0.908992
Partial score of fold 4 is: 0.9089922059208284
Our oof AUC score is:  0.9072729932313089
auc:  0.9072729932313089
|  22       |  0.9073   |  0.5349   |  0.000937 |  4.744    |  0.01289  |  15.78    |  1.033    |  1.074    |
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.900384	valid_1's auc: 0.886926
[200]	training's auc: 0.911412	valid_1's auc: 0.893156
[300]	training's auc: 0.919688	valid_1's auc: 0.897886
[400]	training's auc: 0.926297	valid_1's auc: 0.900911
[500]	training's auc: 0.931785	valid_1's auc: 0.903018
[600]	training's auc: 0.936519	valid_1's auc: 0.904555
[700]	training's auc: 0.940962	valid_1's auc: 0.905724
[800]	training's auc: 0.94499	valid_1's auc: 0.906593
[900]	training's auc: 0.948627	valid_1's auc: 0.907074
[1000]	training's auc: 0.951923	valid_1's auc: 0.907522
[1100]	training's auc: 0.955101	valid_1's auc: 0.90791
[1200]	training's auc: 0.95789	valid_1's auc: 0.908255
[1300]	training's auc: 0.960529	valid_1's auc: 0.908532
[1400]	training's auc: 0.963035	valid_1's auc: 0.908878
[1500]	training's auc: 0.965388	valid_1's auc: 0.909148
[1600]	training's auc: 0.967568	valid_1's auc: 0.909199
[1700]	training's auc: 0.969739	valid_1's auc: 0.909308
[1800]	training's auc: 0.971697	valid_1's auc: 0.909374
[1900]	training's auc: 0.973498	valid_1's auc: 0.909441
[2000]	training's auc: 0.975302	valid_1's auc: 0.909496
[2100]	training's auc: 0.976844	valid_1's auc: 0.909465
[2200]	training's auc: 0.978302	valid_1's auc: 0.909663
[2300]	training's auc: 0.979718	valid_1's auc: 0.909665
Early stopping, best iteration is:
[2267]	training's auc: 0.979271	valid_1's auc: 0.909712
Partial score of fold 0 is: 0.9097118926180163
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.900842	valid_1's auc: 0.883743
[200]	training's auc: 0.911644	valid_1's auc: 0.890118
[300]	training's auc: 0.920342	valid_1's auc: 0.895378
[400]	training's auc: 0.927078	valid_1's auc: 0.898727
[500]	training's auc: 0.932615	valid_1's auc: 0.90098
[600]	training's auc: 0.937232	valid_1's auc: 0.902495
[700]	training's auc: 0.941537	valid_1's auc: 0.903559
[800]	training's auc: 0.945567	valid_1's auc: 0.904279
[900]	training's auc: 0.949428	valid_1's auc: 0.904995
[1000]	training's auc: 0.952748	valid_1's auc: 0.905574
[1100]	training's auc: 0.955757	valid_1's auc: 0.905966
[1200]	training's auc: 0.958675	valid_1's auc: 0.90626
[1300]	training's auc: 0.961337	valid_1's auc: 0.906502
[1400]	training's auc: 0.963869	valid_1's auc: 0.906693
[1500]	training's auc: 0.966213	valid_1's auc: 0.906837
[1600]	training's auc: 0.968379	valid_1's auc: 0.906802
[1700]	training's auc: 0.970503	valid_1's auc: 0.906926
[1800]	training's auc: 0.972421	valid_1's auc: 0.906947
[1900]	training's auc: 0.974232	valid_1's auc: 0.907074
[2000]	training's auc: 0.975949	valid_1's auc: 0.906999
Early stopping, best iteration is:
[1921]	training's auc: 0.974621	valid_1's auc: 0.907093
Partial score of fold 1 is: 0.9070934918593588
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.901868	valid_1's auc: 0.882791
[200]	training's auc: 0.912267	valid_1's auc: 0.888304
[300]	training's auc: 0.920514	valid_1's auc: 0.893181
[400]	training's auc: 0.927213	valid_1's auc: 0.896617
[500]	training's auc: 0.932709	valid_1's auc: 0.898582
[600]	training's auc: 0.937416	valid_1's auc: 0.899681
[700]	training's auc: 0.941634	valid_1's auc: 0.900503
[800]	training's auc: 0.945609	valid_1's auc: 0.901255
[900]	training's auc: 0.949315	valid_1's auc: 0.901745
[1000]	training's auc: 0.952553	valid_1's auc: 0.902157
[1100]	training's auc: 0.955804	valid_1's auc: 0.902616
[1200]	training's auc: 0.958724	valid_1's auc: 0.902811
[1300]	training's auc: 0.961375	valid_1's auc: 0.90298
[1400]	training's auc: 0.963798	valid_1's auc: 0.903185
[1500]	training's auc: 0.966194	valid_1's auc: 0.903442
[1600]	training's auc: 0.968384	valid_1's auc: 0.90363
Early stopping, best iteration is:
[1591]	training's auc: 0.968191	valid_1's auc: 0.903657
Partial score of fold 2 is: 0.9036565039945603
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.90097	valid_1's auc: 0.888864
[200]	training's auc: 0.911739	valid_1's auc: 0.894544
[300]	training's auc: 0.920598	valid_1's auc: 0.898848
[400]	training's auc: 0.927345	valid_1's auc: 0.901266
[500]	training's auc: 0.932741	valid_1's auc: 0.90289
[600]	training's auc: 0.937496	valid_1's auc: 0.903961
[700]	training's auc: 0.941812	valid_1's auc: 0.904851
[800]	training's auc: 0.945869	valid_1's auc: 0.905666
[900]	training's auc: 0.94962	valid_1's auc: 0.906038
[1000]	training's auc: 0.952911	valid_1's auc: 0.906424
[1100]	training's auc: 0.95598	valid_1's auc: 0.906525
[1200]	training's auc: 0.958895	valid_1's auc: 0.906652
[1300]	training's auc: 0.961582	valid_1's auc: 0.906788
[1400]	training's auc: 0.964116	valid_1's auc: 0.906869
[1500]	training's auc: 0.966403	valid_1's auc: 0.90692
[1600]	training's auc: 0.968544	valid_1's auc: 0.906965
Early stopping, best iteration is:
[1559]	training's auc: 0.967676	valid_1's auc: 0.907051
Partial score of fold 3 is: 0.9070508950848182
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.901057	valid_1's auc: 0.885895
[200]	training's auc: 0.911302	valid_1's auc: 0.891807
[300]	training's auc: 0.920005	valid_1's auc: 0.897141
[400]	training's auc: 0.926752	valid_1's auc: 0.900837
[500]	training's auc: 0.932276	valid_1's auc: 0.902904
[600]	training's auc: 0.937078	valid_1's auc: 0.904277
[700]	training's auc: 0.94153	valid_1's auc: 0.905141
[800]	training's auc: 0.94548	valid_1's auc: 0.905882
[900]	training's auc: 0.949127	valid_1's auc: 0.906369
[1000]	training's auc: 0.952594	valid_1's auc: 0.906868
[1100]	training's auc: 0.955683	valid_1's auc: 0.907271
[1200]	training's auc: 0.958546	valid_1's auc: 0.907649
[1300]	training's auc: 0.961273	valid_1's auc: 0.907826
[1400]	training's auc: 0.963742	valid_1's auc: 0.908108
[1500]	training's auc: 0.966123	valid_1's auc: 0.9083
[1600]	training's auc: 0.968386	valid_1's auc: 0.908514
[1700]	training's auc: 0.970366	valid_1's auc: 0.908591
Early stopping, best iteration is:
[1694]	training's auc: 0.97026	valid_1's auc: 0.908616
Partial score of fold 4 is: 0.9086156816316571
Our oof AUC score is:  0.9071923146051201
auc:  0.9071923146051201
|  23       |  0.9072   |  0.6347   |  4.852    |  4.974    |  0.01105  |  16.46    |  1.033    |  1.584    |
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.907658	valid_1's auc: 0.890755
[200]	training's auc: 0.921588	valid_1's auc: 0.898835
[300]	training's auc: 0.93128	valid_1's auc: 0.902778
[400]	training's auc: 0.938797	valid_1's auc: 0.904983
[500]	training's auc: 0.945603	valid_1's auc: 0.906377
[600]	training's auc: 0.951234	valid_1's auc: 0.907292
[700]	training's auc: 0.956391	valid_1's auc: 0.907766
[800]	training's auc: 0.960759	valid_1's auc: 0.908187
[900]	training's auc: 0.964757	valid_1's auc: 0.908638
[1000]	training's auc: 0.96829	valid_1's auc: 0.908906
[1100]	training's auc: 0.971422	valid_1's auc: 0.909069
[1200]	training's auc: 0.974352	valid_1's auc: 0.909196
[1300]	training's auc: 0.976959	valid_1's auc: 0.909117
Early stopping, best iteration is:
[1214]	training's auc: 0.974746	valid_1's auc: 0.909251
Partial score of fold 0 is: 0.9092513007386055
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.908269	valid_1's auc: 0.888073
[200]	training's auc: 0.922177	valid_1's auc: 0.895876
[300]	training's auc: 0.931902	valid_1's auc: 0.90014
[400]	training's auc: 0.939497	valid_1's auc: 0.902304
[500]	training's auc: 0.945977	valid_1's auc: 0.903725
[600]	training's auc: 0.951895	valid_1's auc: 0.904658
[700]	training's auc: 0.95684	valid_1's auc: 0.905256
[800]	training's auc: 0.96138	valid_1's auc: 0.905748
[900]	training's auc: 0.96548	valid_1's auc: 0.905994
[1000]	training's auc: 0.968935	valid_1's auc: 0.906283
[1100]	training's auc: 0.971985	valid_1's auc: 0.906348
[1200]	training's auc: 0.974889	valid_1's auc: 0.906374
Early stopping, best iteration is:
[1171]	training's auc: 0.974075	valid_1's auc: 0.906507
Partial score of fold 1 is: 0.9065073491165834
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.908537	valid_1's auc: 0.885837
[200]	training's auc: 0.922798	valid_1's auc: 0.893933
[300]	training's auc: 0.932722	valid_1's auc: 0.897698
[400]	training's auc: 0.940185	valid_1's auc: 0.899604
[500]	training's auc: 0.9468	valid_1's auc: 0.900921
[600]	training's auc: 0.952464	valid_1's auc: 0.90141
[700]	training's auc: 0.957417	valid_1's auc: 0.901944
[800]	training's auc: 0.961838	valid_1's auc: 0.902337
[900]	training's auc: 0.965744	valid_1's auc: 0.90246
[1000]	training's auc: 0.96925	valid_1's auc: 0.902729
[1100]	training's auc: 0.972317	valid_1's auc: 0.902732
[1200]	training's auc: 0.975246	valid_1's auc: 0.902867
[1300]	training's auc: 0.977792	valid_1's auc: 0.902879
Early stopping, best iteration is:
[1276]	training's auc: 0.977214	valid_1's auc: 0.902974
Partial score of fold 2 is: 0.9029737575703666
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.90842	valid_1's auc: 0.891367
[200]	training's auc: 0.922434	valid_1's auc: 0.898461
[300]	training's auc: 0.932154	valid_1's auc: 0.901821
[400]	training's auc: 0.939803	valid_1's auc: 0.903552
[500]	training's auc: 0.946274	valid_1's auc: 0.904684
[600]	training's auc: 0.952112	valid_1's auc: 0.905302
[700]	training's auc: 0.957075	valid_1's auc: 0.905544
[800]	training's auc: 0.96149	valid_1's auc: 0.905973
[900]	training's auc: 0.965506	valid_1's auc: 0.905938
Early stopping, best iteration is:
[841]	training's auc: 0.963155	valid_1's auc: 0.906045
Partial score of fold 3 is: 0.9060449581837153
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.907826	valid_1's auc: 0.889883
[200]	training's auc: 0.92163	valid_1's auc: 0.898166
[300]	training's auc: 0.931782	valid_1's auc: 0.902257
[400]	training's auc: 0.939354	valid_1's auc: 0.90418
[500]	training's auc: 0.945936	valid_1's auc: 0.905563
[600]	training's auc: 0.951944	valid_1's auc: 0.906596
[700]	training's auc: 0.956962	valid_1's auc: 0.907069
[800]	training's auc: 0.961353	valid_1's auc: 0.907492
[900]	training's auc: 0.965291	valid_1's auc: 0.907663
[1000]	training's auc: 0.9688	valid_1's auc: 0.907883
[1100]	training's auc: 0.972066	valid_1's auc: 0.908053
[1200]	training's auc: 0.974825	valid_1's auc: 0.908133
Early stopping, best iteration is:
[1195]	training's auc: 0.974702	valid_1's auc: 0.908163
Partial score of fold 4 is: 0.9081630533741367
Our oof AUC score is:  0.9065272899964882
auc:  0.9065272899964882
|  24       |  0.9065   |  0.6786   |  4.858    |  4.331    |  0.0178   |  16.3     |  1.002    |  1.337    |
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.911504	valid_1's auc: 0.891934
[200]	training's auc: 0.927062	valid_1's auc: 0.900349
[300]	training's auc: 0.937488	valid_1's auc: 0.903746
[400]	training's auc: 0.946002	valid_1's auc: 0.905751
[500]	training's auc: 0.953366	valid_1's auc: 0.90708
[600]	training's auc: 0.959603	valid_1's auc: 0.908038
[700]	training's auc: 0.96512	valid_1's auc: 0.908465
[800]	training's auc: 0.969897	valid_1's auc: 0.908762
[900]	training's auc: 0.974001	valid_1's auc: 0.908934
[1000]	training's auc: 0.977379	valid_1's auc: 0.909043
[1100]	training's auc: 0.980496	valid_1's auc: 0.909043
Early stopping, best iteration is:
[1048]	training's auc: 0.978959	valid_1's auc: 0.90911
Partial score of fold 0 is: 0.9091095424686821
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.91187	valid_1's auc: 0.889823
[200]	training's auc: 0.927302	valid_1's auc: 0.897802
[300]	training's auc: 0.938004	valid_1's auc: 0.901535
[400]	training's auc: 0.946579	valid_1's auc: 0.903189
[500]	training's auc: 0.954111	valid_1's auc: 0.904489
[600]	training's auc: 0.960464	valid_1's auc: 0.905338
[700]	training's auc: 0.965707	valid_1's auc: 0.905778
[800]	training's auc: 0.970389	valid_1's auc: 0.905821
[900]	training's auc: 0.974549	valid_1's auc: 0.905866
Early stopping, best iteration is:
[839]	training's auc: 0.972185	valid_1's auc: 0.906003
Partial score of fold 1 is: 0.9060033741558958
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.911954	valid_1's auc: 0.887559
[200]	training's auc: 0.92738	valid_1's auc: 0.895676
[300]	training's auc: 0.938125	valid_1's auc: 0.898984
[400]	training's auc: 0.946644	valid_1's auc: 0.900679
[500]	training's auc: 0.954061	valid_1's auc: 0.901682
[600]	training's auc: 0.960357	valid_1's auc: 0.902364
[700]	training's auc: 0.965645	valid_1's auc: 0.902659
[800]	training's auc: 0.970366	valid_1's auc: 0.90283
[900]	training's auc: 0.974374	valid_1's auc: 0.902887
[1000]	training's auc: 0.977768	valid_1's auc: 0.902974
[1100]	training's auc: 0.98079	valid_1's auc: 0.903284
[1200]	training's auc: 0.983609	valid_1's auc: 0.903272
Early stopping, best iteration is:
[1123]	training's auc: 0.981453	valid_1's auc: 0.903352
Partial score of fold 2 is: 0.9033515409097558
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.911878	valid_1's auc: 0.893105
[200]	training's auc: 0.927489	valid_1's auc: 0.899985
[300]	training's auc: 0.93811	valid_1's auc: 0.902878
[400]	training's auc: 0.946648	valid_1's auc: 0.904508
[500]	training's auc: 0.953929	valid_1's auc: 0.905445
[600]	training's auc: 0.960329	valid_1's auc: 0.905893
[700]	training's auc: 0.965659	valid_1's auc: 0.906364
[800]	training's auc: 0.970316	valid_1's auc: 0.90679
[900]	training's auc: 0.974427	valid_1's auc: 0.907077
[1000]	training's auc: 0.977947	valid_1's auc: 0.907178
Early stopping, best iteration is:
[927]	training's auc: 0.975417	valid_1's auc: 0.907285
Partial score of fold 3 is: 0.9072848610736947
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.911391	valid_1's auc: 0.890379
[200]	training's auc: 0.926736	valid_1's auc: 0.899089
[300]	training's auc: 0.937571	valid_1's auc: 0.90309
[400]	training's auc: 0.946036	valid_1's auc: 0.904917
[500]	training's auc: 0.953501	valid_1's auc: 0.906221
[600]	training's auc: 0.959928	valid_1's auc: 0.907057
[700]	training's auc: 0.965328	valid_1's auc: 0.907238
[800]	training's auc: 0.969884	valid_1's auc: 0.907524
[900]	training's auc: 0.974002	valid_1's auc: 0.907856
[1000]	training's auc: 0.977424	valid_1's auc: 0.908011
[1100]	training's auc: 0.980586	valid_1's auc: 0.908145
[1200]	training's auc: 0.983449	valid_1's auc: 0.908258
[1300]	training's auc: 0.985859	valid_1's auc: 0.908224
Early stopping, best iteration is:
[1210]	training's auc: 0.983715	valid_1's auc: 0.908322
Partial score of fold 4 is: 0.9083223477625679
Our oof AUC score is:  0.9067660511469412
auc:  0.9067660511469412
|  25       |  0.9068   |  0.7097   |  0.09637  |  4.677    |  0.01887  |  16.92    |  1.107    |  1.024    |
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.899358	valid_1's auc: 0.886976
[200]	training's auc: 0.907669	valid_1's auc: 0.891791
[300]	training's auc: 0.914542	valid_1's auc: 0.895347
[400]	training's auc: 0.920361	valid_1's auc: 0.898506
[500]	training's auc: 0.92523	valid_1's auc: 0.900896
[600]	training's auc: 0.929528	valid_1's auc: 0.902815
[700]	training's auc: 0.933281	valid_1's auc: 0.904161
[800]	training's auc: 0.936711	valid_1's auc: 0.905167
[900]	training's auc: 0.939914	valid_1's auc: 0.905877
[1000]	training's auc: 0.942914	valid_1's auc: 0.906541
[1100]	training's auc: 0.945717	valid_1's auc: 0.906978
[1200]	training's auc: 0.948436	valid_1's auc: 0.907454
[1300]	training's auc: 0.950997	valid_1's auc: 0.907894
[1400]	training's auc: 0.953357	valid_1's auc: 0.90822
[1500]	training's auc: 0.955656	valid_1's auc: 0.90851
[1600]	training's auc: 0.957823	valid_1's auc: 0.90876
[1700]	training's auc: 0.959913	valid_1's auc: 0.908919
[1800]	training's auc: 0.961929	valid_1's auc: 0.9091
[1900]	training's auc: 0.963788	valid_1's auc: 0.909298
[2000]	training's auc: 0.965544	valid_1's auc: 0.909451
[2100]	training's auc: 0.967185	valid_1's auc: 0.909556
[2200]	training's auc: 0.968755	valid_1's auc: 0.909665
[2300]	training's auc: 0.970308	valid_1's auc: 0.909798
[2400]	training's auc: 0.971786	valid_1's auc: 0.909794
[2500]	training's auc: 0.973125	valid_1's auc: 0.909857
[2600]	training's auc: 0.97446	valid_1's auc: 0.909923
[2700]	training's auc: 0.975661	valid_1's auc: 0.909942
Early stopping, best iteration is:
[2643]	training's auc: 0.974999	valid_1's auc: 0.909959
Partial score of fold 0 is: 0.9099590744138573
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.899598	valid_1's auc: 0.885021
[200]	training's auc: 0.907759	valid_1's auc: 0.889878
[300]	training's auc: 0.91485	valid_1's auc: 0.893416
[400]	training's auc: 0.920711	valid_1's auc: 0.896422
[500]	training's auc: 0.925693	valid_1's auc: 0.898995
[600]	training's auc: 0.929923	valid_1's auc: 0.900811
[700]	training's auc: 0.933601	valid_1's auc: 0.902158
[800]	training's auc: 0.936951	valid_1's auc: 0.903225
[900]	training's auc: 0.940162	valid_1's auc: 0.904073
[1000]	training's auc: 0.943143	valid_1's auc: 0.90483
[1100]	training's auc: 0.945996	valid_1's auc: 0.905314
[1200]	training's auc: 0.948736	valid_1's auc: 0.905832
[1300]	training's auc: 0.951279	valid_1's auc: 0.906225
[1400]	training's auc: 0.953673	valid_1's auc: 0.906559
[1500]	training's auc: 0.955977	valid_1's auc: 0.906753
[1600]	training's auc: 0.958108	valid_1's auc: 0.906796
[1700]	training's auc: 0.960178	valid_1's auc: 0.906925
[1800]	training's auc: 0.962136	valid_1's auc: 0.907044
[1900]	training's auc: 0.964001	valid_1's auc: 0.907168
[2000]	training's auc: 0.965698	valid_1's auc: 0.907241
[2100]	training's auc: 0.967362	valid_1's auc: 0.907282
Early stopping, best iteration is:
[2032]	training's auc: 0.966251	valid_1's auc: 0.907315
Partial score of fold 1 is: 0.9073147418047061
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.900782	valid_1's auc: 0.882618
[200]	training's auc: 0.908633	valid_1's auc: 0.887012
[300]	training's auc: 0.915261	valid_1's auc: 0.890628
[400]	training's auc: 0.92104	valid_1's auc: 0.893789
[500]	training's auc: 0.925891	valid_1's auc: 0.896184
[600]	training's auc: 0.930131	valid_1's auc: 0.897768
[700]	training's auc: 0.933908	valid_1's auc: 0.898875
[800]	training's auc: 0.937272	valid_1's auc: 0.899895
[900]	training's auc: 0.94044	valid_1's auc: 0.900628
[1000]	training's auc: 0.943377	valid_1's auc: 0.901196
[1100]	training's auc: 0.946279	valid_1's auc: 0.901509
[1200]	training's auc: 0.948943	valid_1's auc: 0.901901
[1300]	training's auc: 0.951434	valid_1's auc: 0.902266
[1400]	training's auc: 0.953821	valid_1's auc: 0.902558
[1500]	training's auc: 0.95614	valid_1's auc: 0.902864
[1600]	training's auc: 0.958298	valid_1's auc: 0.903075
[1700]	training's auc: 0.960317	valid_1's auc: 0.903315
[1800]	training's auc: 0.962192	valid_1's auc: 0.903397
[1900]	training's auc: 0.964027	valid_1's auc: 0.903598
[2000]	training's auc: 0.965784	valid_1's auc: 0.903705
[2100]	training's auc: 0.967416	valid_1's auc: 0.903731
[2200]	training's auc: 0.969026	valid_1's auc: 0.903885
[2300]	training's auc: 0.970572	valid_1's auc: 0.903897
[2400]	training's auc: 0.971953	valid_1's auc: 0.904012
[2500]	training's auc: 0.973276	valid_1's auc: 0.904062
[2600]	training's auc: 0.974591	valid_1's auc: 0.90408
[2700]	training's auc: 0.975829	valid_1's auc: 0.904138
[2800]	training's auc: 0.977045	valid_1's auc: 0.904177
[2900]	training's auc: 0.978182	valid_1's auc: 0.904241
[3000]	training's auc: 0.979279	valid_1's auc: 0.904186
Early stopping, best iteration is:
[2915]	training's auc: 0.978346	valid_1's auc: 0.904257
Partial score of fold 2 is: 0.9042566303369483
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.899869	valid_1's auc: 0.888264
[200]	training's auc: 0.907982	valid_1's auc: 0.892879
[300]	training's auc: 0.914798	valid_1's auc: 0.896127
[400]	training's auc: 0.920801	valid_1's auc: 0.898862
[500]	training's auc: 0.925628	valid_1's auc: 0.90076
[600]	training's auc: 0.929934	valid_1's auc: 0.902154
[700]	training's auc: 0.933667	valid_1's auc: 0.903222
[800]	training's auc: 0.937023	valid_1's auc: 0.904127
[900]	training's auc: 0.940284	valid_1's auc: 0.904678
[1000]	training's auc: 0.943252	valid_1's auc: 0.90518
[1100]	training's auc: 0.946159	valid_1's auc: 0.905606
[1200]	training's auc: 0.948909	valid_1's auc: 0.905944
[1300]	training's auc: 0.951474	valid_1's auc: 0.906274
[1400]	training's auc: 0.953954	valid_1's auc: 0.90652
[1500]	training's auc: 0.956242	valid_1's auc: 0.906657
[1600]	training's auc: 0.958421	valid_1's auc: 0.906822
[1700]	training's auc: 0.960432	valid_1's auc: 0.907026
[1800]	training's auc: 0.962328	valid_1's auc: 0.907088
[1900]	training's auc: 0.964192	valid_1's auc: 0.907162
[2000]	training's auc: 0.965914	valid_1's auc: 0.907201
Early stopping, best iteration is:
[1967]	training's auc: 0.965354	valid_1's auc: 0.907245
Partial score of fold 3 is: 0.907244867854072
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.899802	valid_1's auc: 0.885389
[200]	training's auc: 0.907617	valid_1's auc: 0.890191
[300]	training's auc: 0.91444	valid_1's auc: 0.893913
[400]	training's auc: 0.920195	valid_1's auc: 0.897459
[500]	training's auc: 0.925221	valid_1's auc: 0.900295
[600]	training's auc: 0.929517	valid_1's auc: 0.902334
[700]	training's auc: 0.933258	valid_1's auc: 0.903623
[800]	training's auc: 0.936676	valid_1's auc: 0.904537
[900]	training's auc: 0.939914	valid_1's auc: 0.905354
[1000]	training's auc: 0.942962	valid_1's auc: 0.906036
[1100]	training's auc: 0.945841	valid_1's auc: 0.906568
[1200]	training's auc: 0.948608	valid_1's auc: 0.907135
[1300]	training's auc: 0.951153	valid_1's auc: 0.907547
[1400]	training's auc: 0.953636	valid_1's auc: 0.907967
[1500]	training's auc: 0.955902	valid_1's auc: 0.908143
[1600]	training's auc: 0.958035	valid_1's auc: 0.908402
[1700]	training's auc: 0.960098	valid_1's auc: 0.908647
[1800]	training's auc: 0.962062	valid_1's auc: 0.908781
[1900]	training's auc: 0.963887	valid_1's auc: 0.908894
[2000]	training's auc: 0.965633	valid_1's auc: 0.908983
[2100]	training's auc: 0.967299	valid_1's auc: 0.90909
[2200]	training's auc: 0.968846	valid_1's auc: 0.909291
[2300]	training's auc: 0.970359	valid_1's auc: 0.909271
Early stopping, best iteration is:
[2237]	training's auc: 0.969396	valid_1's auc: 0.909322
Partial score of fold 4 is: 0.9093215751508595
Our oof AUC score is:  0.9075241899054636
auc:  0.9075241899054636
|  26       |  0.9075   |  0.5242   |  4.172    |  4.644    |  0.007898 |  16.62    |  1.22     |  1.028    |
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.908407	valid_1's auc: 0.890321
[200]	training's auc: 0.923382	valid_1's auc: 0.898823
[300]	training's auc: 0.932987	valid_1's auc: 0.902705
[400]	training's auc: 0.940741	valid_1's auc: 0.904898
[500]	training's auc: 0.947469	valid_1's auc: 0.906222
[600]	training's auc: 0.953194	valid_1's auc: 0.907216
[700]	training's auc: 0.958393	valid_1's auc: 0.907814
[800]	training's auc: 0.962843	valid_1's auc: 0.908129
[900]	training's auc: 0.966857	valid_1's auc: 0.908471
[1000]	training's auc: 0.970367	valid_1's auc: 0.908681
[1100]	training's auc: 0.97358	valid_1's auc: 0.908882
[1200]	training's auc: 0.976528	valid_1's auc: 0.908947
[1300]	training's auc: 0.979035	valid_1's auc: 0.90898
[1400]	training's auc: 0.981294	valid_1's auc: 0.908869
Early stopping, best iteration is:
[1315]	training's auc: 0.979415	valid_1's auc: 0.909043
Partial score of fold 0 is: 0.9090426398020737
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.908537	valid_1's auc: 0.887786
[200]	training's auc: 0.923184	valid_1's auc: 0.896164
[300]	training's auc: 0.933263	valid_1's auc: 0.900426
[400]	training's auc: 0.941014	valid_1's auc: 0.902331
[500]	training's auc: 0.947691	valid_1's auc: 0.903619
[600]	training's auc: 0.953611	valid_1's auc: 0.904335
[700]	training's auc: 0.958697	valid_1's auc: 0.904808
[800]	training's auc: 0.963293	valid_1's auc: 0.905162
[900]	training's auc: 0.967137	valid_1's auc: 0.905382
[1000]	training's auc: 0.97073	valid_1's auc: 0.90566
[1100]	training's auc: 0.973923	valid_1's auc: 0.90561
Early stopping, best iteration is:
[1038]	training's auc: 0.971941	valid_1's auc: 0.9057
Partial score of fold 1 is: 0.9056998056618878
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.908866	valid_1's auc: 0.885518
[200]	training's auc: 0.923802	valid_1's auc: 0.893547
[300]	training's auc: 0.933845	valid_1's auc: 0.897436
[400]	training's auc: 0.941673	valid_1's auc: 0.898867
[500]	training's auc: 0.948531	valid_1's auc: 0.89974
[600]	training's auc: 0.954232	valid_1's auc: 0.900702
[700]	training's auc: 0.959143	valid_1's auc: 0.901135
[800]	training's auc: 0.96357	valid_1's auc: 0.901402
Early stopping, best iteration is:
[769]	training's auc: 0.962195	valid_1's auc: 0.901516
Partial score of fold 2 is: 0.9015157694296652
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.908786	valid_1's auc: 0.891511
[200]	training's auc: 0.923641	valid_1's auc: 0.898708
[300]	training's auc: 0.933698	valid_1's auc: 0.902063
[400]	training's auc: 0.941417	valid_1's auc: 0.903723
[500]	training's auc: 0.947947	valid_1's auc: 0.904799
[600]	training's auc: 0.953893	valid_1's auc: 0.905344
[700]	training's auc: 0.958967	valid_1's auc: 0.905738
[800]	training's auc: 0.963422	valid_1's auc: 0.906055
Early stopping, best iteration is:
[796]	training's auc: 0.963266	valid_1's auc: 0.906075
Partial score of fold 3 is: 0.9060751132974743
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.908077	valid_1's auc: 0.888192
[200]	training's auc: 0.922997	valid_1's auc: 0.89722
[300]	training's auc: 0.933057	valid_1's auc: 0.901585
[400]	training's auc: 0.940796	valid_1's auc: 0.903284
[500]	training's auc: 0.947586	valid_1's auc: 0.904788
[600]	training's auc: 0.953461	valid_1's auc: 0.905619
[700]	training's auc: 0.958473	valid_1's auc: 0.905907
[800]	training's auc: 0.962865	valid_1's auc: 0.906373
[900]	training's auc: 0.966964	valid_1's auc: 0.906583
[1000]	training's auc: 0.970526	valid_1's auc: 0.906737
[1100]	training's auc: 0.973654	valid_1's auc: 0.906723
[1200]	training's auc: 0.976443	valid_1's auc: 0.906708
Early stopping, best iteration is:
[1128]	training's auc: 0.974491	valid_1's auc: 0.906884
Partial score of fold 4 is: 0.9068841749996239
Our oof AUC score is:  0.9058214006407159
auc:  0.9058214006407159
|  27       |  0.9058   |  0.7671   |  4.621    |  4.803    |  0.01787  |  16.01    |  1.036    |  1.079    |
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.897702	valid_1's auc: 0.885156
[200]	training's auc: 0.905142	valid_1's auc: 0.88931
[300]	training's auc: 0.911545	valid_1's auc: 0.892411
[400]	training's auc: 0.917076	valid_1's auc: 0.895346
[500]	training's auc: 0.92186	valid_1's auc: 0.897907
[600]	training's auc: 0.925875	valid_1's auc: 0.899846
[700]	training's auc: 0.929499	valid_1's auc: 0.901336
[800]	training's auc: 0.932624	valid_1's auc: 0.902459
[900]	training's auc: 0.935602	valid_1's auc: 0.903265
[1000]	training's auc: 0.938306	valid_1's auc: 0.904114
[1100]	training's auc: 0.940932	valid_1's auc: 0.904802
[1200]	training's auc: 0.943343	valid_1's auc: 0.905374
[1300]	training's auc: 0.945782	valid_1's auc: 0.905854
[1400]	training's auc: 0.948036	valid_1's auc: 0.906245
[1500]	training's auc: 0.950289	valid_1's auc: 0.906604
[1600]	training's auc: 0.952326	valid_1's auc: 0.907008
[1700]	training's auc: 0.954317	valid_1's auc: 0.907361
[1800]	training's auc: 0.956206	valid_1's auc: 0.90759
[1900]	training's auc: 0.958	valid_1's auc: 0.907802
[2000]	training's auc: 0.959737	valid_1's auc: 0.907964
[2100]	training's auc: 0.961361	valid_1's auc: 0.908111
[2200]	training's auc: 0.96288	valid_1's auc: 0.908235
[2300]	training's auc: 0.964372	valid_1's auc: 0.908481
[2400]	training's auc: 0.965852	valid_1's auc: 0.908639
[2500]	training's auc: 0.96724	valid_1's auc: 0.908794
[2600]	training's auc: 0.968523	valid_1's auc: 0.908861
[2700]	training's auc: 0.969746	valid_1's auc: 0.908978
[2800]	training's auc: 0.970945	valid_1's auc: 0.909035
[2900]	training's auc: 0.972142	valid_1's auc: 0.909085
[3000]	training's auc: 0.973263	valid_1's auc: 0.909174
[3100]	training's auc: 0.974338	valid_1's auc: 0.909198
[3200]	training's auc: 0.975318	valid_1's auc: 0.909265
Early stopping, best iteration is:
[3193]	training's auc: 0.975252	valid_1's auc: 0.909269
Partial score of fold 0 is: 0.9092687896610315
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.898199	valid_1's auc: 0.882233
[200]	training's auc: 0.905683	valid_1's auc: 0.886767
[300]	training's auc: 0.912295	valid_1's auc: 0.890263
[400]	training's auc: 0.917666	valid_1's auc: 0.893102
[500]	training's auc: 0.922504	valid_1's auc: 0.895868
[600]	training's auc: 0.926654	valid_1's auc: 0.897821
[700]	training's auc: 0.930152	valid_1's auc: 0.899393
[800]	training's auc: 0.933277	valid_1's auc: 0.900604
[900]	training's auc: 0.936246	valid_1's auc: 0.901449
[1000]	training's auc: 0.938978	valid_1's auc: 0.902213
[1100]	training's auc: 0.941524	valid_1's auc: 0.902868
[1200]	training's auc: 0.943968	valid_1's auc: 0.903509
[1300]	training's auc: 0.946338	valid_1's auc: 0.904001
[1400]	training's auc: 0.948592	valid_1's auc: 0.904396
[1500]	training's auc: 0.950823	valid_1's auc: 0.904713
[1600]	training's auc: 0.952867	valid_1's auc: 0.904941
[1700]	training's auc: 0.95488	valid_1's auc: 0.905198
[1800]	training's auc: 0.956721	valid_1's auc: 0.905441
[1900]	training's auc: 0.958589	valid_1's auc: 0.905703
[2000]	training's auc: 0.960225	valid_1's auc: 0.905946
[2100]	training's auc: 0.961852	valid_1's auc: 0.906004
[2200]	training's auc: 0.963447	valid_1's auc: 0.906143
[2300]	training's auc: 0.964922	valid_1's auc: 0.906233
[2400]	training's auc: 0.966358	valid_1's auc: 0.906344
[2500]	training's auc: 0.967748	valid_1's auc: 0.906484
[2600]	training's auc: 0.969039	valid_1's auc: 0.9066
[2700]	training's auc: 0.97029	valid_1's auc: 0.906715
[2800]	training's auc: 0.971485	valid_1's auc: 0.906823
Early stopping, best iteration is:
[2795]	training's auc: 0.971422	valid_1's auc: 0.906836
Partial score of fold 1 is: 0.906836397161367
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.899387	valid_1's auc: 0.880496
[200]	training's auc: 0.906424	valid_1's auc: 0.884556
[300]	training's auc: 0.912652	valid_1's auc: 0.887948
[400]	training's auc: 0.918055	valid_1's auc: 0.89079
[500]	training's auc: 0.922782	valid_1's auc: 0.893557
[600]	training's auc: 0.92681	valid_1's auc: 0.895549
[700]	training's auc: 0.930328	valid_1's auc: 0.896961
[800]	training's auc: 0.933489	valid_1's auc: 0.898086
[900]	training's auc: 0.936437	valid_1's auc: 0.898802
[1000]	training's auc: 0.939137	valid_1's auc: 0.899504
[1100]	training's auc: 0.941755	valid_1's auc: 0.90009
[1200]	training's auc: 0.944228	valid_1's auc: 0.90044
[1300]	training's auc: 0.946574	valid_1's auc: 0.900757
[1400]	training's auc: 0.948792	valid_1's auc: 0.901081
[1500]	training's auc: 0.95093	valid_1's auc: 0.901448
[1600]	training's auc: 0.95301	valid_1's auc: 0.90179
[1700]	training's auc: 0.954975	valid_1's auc: 0.902023
[1800]	training's auc: 0.956793	valid_1's auc: 0.902227
[1900]	training's auc: 0.958641	valid_1's auc: 0.90243
[2000]	training's auc: 0.960329	valid_1's auc: 0.902657
[2100]	training's auc: 0.961958	valid_1's auc: 0.902724
[2200]	training's auc: 0.963462	valid_1's auc: 0.90287
[2300]	training's auc: 0.96498	valid_1's auc: 0.903003
[2400]	training's auc: 0.96637	valid_1's auc: 0.903066
[2500]	training's auc: 0.967778	valid_1's auc: 0.903135
[2600]	training's auc: 0.969063	valid_1's auc: 0.903241
[2700]	training's auc: 0.970278	valid_1's auc: 0.903289
[2800]	training's auc: 0.97154	valid_1's auc: 0.903296
[2900]	training's auc: 0.972693	valid_1's auc: 0.903374
Early stopping, best iteration is:
[2896]	training's auc: 0.972647	valid_1's auc: 0.903381
Partial score of fold 2 is: 0.9033810534663497
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.898153	valid_1's auc: 0.88609
[200]	training's auc: 0.905307	valid_1's auc: 0.890046
[300]	training's auc: 0.911783	valid_1's auc: 0.893143
[400]	training's auc: 0.917409	valid_1's auc: 0.896131
[500]	training's auc: 0.922267	valid_1's auc: 0.898161
[600]	training's auc: 0.926324	valid_1's auc: 0.899753
[700]	training's auc: 0.929872	valid_1's auc: 0.900936
[800]	training's auc: 0.933072	valid_1's auc: 0.901949
[900]	training's auc: 0.936091	valid_1's auc: 0.902726
[1000]	training's auc: 0.938847	valid_1's auc: 0.903293
[1100]	training's auc: 0.94154	valid_1's auc: 0.903836
[1200]	training's auc: 0.944032	valid_1's auc: 0.904241
[1300]	training's auc: 0.946338	valid_1's auc: 0.904651
[1400]	training's auc: 0.948586	valid_1's auc: 0.904946
[1500]	training's auc: 0.950794	valid_1's auc: 0.905215
[1600]	training's auc: 0.95283	valid_1's auc: 0.905387
[1700]	training's auc: 0.954845	valid_1's auc: 0.905626
[1800]	training's auc: 0.956753	valid_1's auc: 0.90576
[1900]	training's auc: 0.958579	valid_1's auc: 0.905831
[2000]	training's auc: 0.960281	valid_1's auc: 0.905901
[2100]	training's auc: 0.961967	valid_1's auc: 0.905981
[2200]	training's auc: 0.963454	valid_1's auc: 0.906105
[2300]	training's auc: 0.96491	valid_1's auc: 0.906214
Early stopping, best iteration is:
[2299]	training's auc: 0.964897	valid_1's auc: 0.906218
Partial score of fold 3 is: 0.9062178223733378
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.898517	valid_1's auc: 0.882938
[200]	training's auc: 0.905466	valid_1's auc: 0.88718
[300]	training's auc: 0.91155	valid_1's auc: 0.890622
[400]	training's auc: 0.916937	valid_1's auc: 0.893911
[500]	training's auc: 0.921711	valid_1's auc: 0.896976
[600]	training's auc: 0.925875	valid_1's auc: 0.899321
[700]	training's auc: 0.929467	valid_1's auc: 0.900883
[800]	training's auc: 0.932619	valid_1's auc: 0.90208
[900]	training's auc: 0.935645	valid_1's auc: 0.902955
[1000]	training's auc: 0.938469	valid_1's auc: 0.903752
[1100]	training's auc: 0.941134	valid_1's auc: 0.904332
[1200]	training's auc: 0.943587	valid_1's auc: 0.904991
[1300]	training's auc: 0.945975	valid_1's auc: 0.905433
[1400]	training's auc: 0.948306	valid_1's auc: 0.905747
[1500]	training's auc: 0.950487	valid_1's auc: 0.906139
[1600]	training's auc: 0.952618	valid_1's auc: 0.906418
[1700]	training's auc: 0.954644	valid_1's auc: 0.906691
[1800]	training's auc: 0.956531	valid_1's auc: 0.906997
[1900]	training's auc: 0.958322	valid_1's auc: 0.90728
[2000]	training's auc: 0.960024	valid_1's auc: 0.907403
[2100]	training's auc: 0.961675	valid_1's auc: 0.90748
[2200]	training's auc: 0.963252	valid_1's auc: 0.907669
[2300]	training's auc: 0.964726	valid_1's auc: 0.907776
[2400]	training's auc: 0.966133	valid_1's auc: 0.907902
[2500]	training's auc: 0.967493	valid_1's auc: 0.908013
[2600]	training's auc: 0.968822	valid_1's auc: 0.908042
[2700]	training's auc: 0.970084	valid_1's auc: 0.908188
[2800]	training's auc: 0.97127	valid_1's auc: 0.908235
[2900]	training's auc: 0.972439	valid_1's auc: 0.908273
Early stopping, best iteration is:
[2893]	training's auc: 0.972358	valid_1's auc: 0.908286
Partial score of fold 4 is: 0.9082858223810274
Our oof AUC score is:  0.9067461313919523
auc:  0.9067461313919523
|  28       |  0.9067   |  0.6594   |  4.883    |  4.989    |  0.006024 |  16.74    |  1.51     |  1.039    |
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.908265	valid_1's auc: 0.890778
[200]	training's auc: 0.922089	valid_1's auc: 0.8983
[300]	training's auc: 0.931866	valid_1's auc: 0.902161
[400]	training's auc: 0.93978	valid_1's auc: 0.904473
[500]	training's auc: 0.94656	valid_1's auc: 0.905546
[600]	training's auc: 0.952498	valid_1's auc: 0.906734
[700]	training's auc: 0.957917	valid_1's auc: 0.90759
[800]	training's auc: 0.962562	valid_1's auc: 0.908246
[900]	training's auc: 0.966879	valid_1's auc: 0.908699
[1000]	training's auc: 0.970424	valid_1's auc: 0.908813
[1100]	training's auc: 0.973679	valid_1's auc: 0.90906
[1200]	training's auc: 0.9766	valid_1's auc: 0.90908
Early stopping, best iteration is:
[1156]	training's auc: 0.975321	valid_1's auc: 0.909098
Partial score of fold 0 is: 0.9090980842091614
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.908547	valid_1's auc: 0.888723
[200]	training's auc: 0.922251	valid_1's auc: 0.896429
[300]	training's auc: 0.932186	valid_1's auc: 0.9009
[400]	training's auc: 0.939869	valid_1's auc: 0.903173
[500]	training's auc: 0.946789	valid_1's auc: 0.904644
[600]	training's auc: 0.952808	valid_1's auc: 0.905736
[700]	training's auc: 0.958052	valid_1's auc: 0.906282
[800]	training's auc: 0.96291	valid_1's auc: 0.906753
[900]	training's auc: 0.967181	valid_1's auc: 0.906935
[1000]	training's auc: 0.970843	valid_1's auc: 0.907035
[1100]	training's auc: 0.974024	valid_1's auc: 0.907343
[1200]	training's auc: 0.977062	valid_1's auc: 0.907537
[1300]	training's auc: 0.979635	valid_1's auc: 0.90755
Early stopping, best iteration is:
[1227]	training's auc: 0.977821	valid_1's auc: 0.907609
Partial score of fold 1 is: 0.9076093019959987
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.909108	valid_1's auc: 0.886255
[200]	training's auc: 0.9228	valid_1's auc: 0.893402
[300]	training's auc: 0.932785	valid_1's auc: 0.897745
[400]	training's auc: 0.9406	valid_1's auc: 0.899737
[500]	training's auc: 0.947384	valid_1's auc: 0.901275
[600]	training's auc: 0.953249	valid_1's auc: 0.901932
[700]	training's auc: 0.958543	valid_1's auc: 0.902527
[800]	training's auc: 0.963008	valid_1's auc: 0.90282
Early stopping, best iteration is:
[760]	training's auc: 0.961296	valid_1's auc: 0.90286
Partial score of fold 2 is: 0.902859815733095
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.908703	valid_1's auc: 0.891953
[200]	training's auc: 0.922413	valid_1's auc: 0.898508
[300]	training's auc: 0.932361	valid_1's auc: 0.901869
[400]	training's auc: 0.940227	valid_1's auc: 0.903782
[500]	training's auc: 0.946892	valid_1's auc: 0.904763
[600]	training's auc: 0.953066	valid_1's auc: 0.905551
[700]	training's auc: 0.958242	valid_1's auc: 0.905933
[800]	training's auc: 0.962888	valid_1's auc: 0.906309
[900]	training's auc: 0.967273	valid_1's auc: 0.906489
Early stopping, best iteration is:
[880]	training's auc: 0.966459	valid_1's auc: 0.906588
Partial score of fold 3 is: 0.9065884664153262
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.908507	valid_1's auc: 0.889904
[200]	training's auc: 0.922099	valid_1's auc: 0.897733
[300]	training's auc: 0.932003	valid_1's auc: 0.902166
[400]	training's auc: 0.939791	valid_1's auc: 0.904581
[500]	training's auc: 0.946591	valid_1's auc: 0.905852
[600]	training's auc: 0.952642	valid_1's auc: 0.90677
[700]	training's auc: 0.957906	valid_1's auc: 0.907341
[800]	training's auc: 0.962536	valid_1's auc: 0.907828
[900]	training's auc: 0.966731	valid_1's auc: 0.908164
[1000]	training's auc: 0.970434	valid_1's auc: 0.908287
[1100]	training's auc: 0.973682	valid_1's auc: 0.90836
[1200]	training's auc: 0.976747	valid_1's auc: 0.908641
[1300]	training's auc: 0.979512	valid_1's auc: 0.908606
Early stopping, best iteration is:
[1210]	training's auc: 0.977049	valid_1's auc: 0.908703
Partial score of fold 4 is: 0.9087031314615576
Our oof AUC score is:  0.9069602265424341
auc:  0.9069602265424341
|  29       |  0.907    |  0.6403   |  0.3946   |  4.887    |  0.01584  |  16.7     |  1.12     |  1.153    |
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.891333	valid_1's auc: 0.878058
[200]	training's auc: 0.899732	valid_1's auc: 0.884128
[300]	training's auc: 0.90646	valid_1's auc: 0.887876
[400]	training's auc: 0.912152	valid_1's auc: 0.890774
[500]	training's auc: 0.91705	valid_1's auc: 0.89347
[600]	training's auc: 0.921557	valid_1's auc: 0.895936
[700]	training's auc: 0.925459	valid_1's auc: 0.897915
[800]	training's auc: 0.928715	valid_1's auc: 0.899359
[900]	training's auc: 0.931738	valid_1's auc: 0.900481
[1000]	training's auc: 0.934468	valid_1's auc: 0.90148
[1100]	training's auc: 0.936937	valid_1's auc: 0.902233
[1200]	training's auc: 0.939326	valid_1's auc: 0.90296
[1300]	training's auc: 0.941541	valid_1's auc: 0.903433
[1400]	training's auc: 0.943631	valid_1's auc: 0.903908
[1500]	training's auc: 0.945674	valid_1's auc: 0.904378
[1600]	training's auc: 0.947665	valid_1's auc: 0.904789
[1700]	training's auc: 0.949474	valid_1's auc: 0.90508
[1800]	training's auc: 0.951337	valid_1's auc: 0.905245
[1900]	training's auc: 0.953027	valid_1's auc: 0.905596
[2000]	training's auc: 0.954708	valid_1's auc: 0.905794
[2100]	training's auc: 0.956239	valid_1's auc: 0.905996
[2200]	training's auc: 0.957785	valid_1's auc: 0.906215
[2300]	training's auc: 0.959247	valid_1's auc: 0.906465
[2400]	training's auc: 0.960669	valid_1's auc: 0.9066
[2500]	training's auc: 0.96205	valid_1's auc: 0.906802
[2600]	training's auc: 0.96335	valid_1's auc: 0.906922
[2700]	training's auc: 0.964589	valid_1's auc: 0.907017
[2800]	training's auc: 0.965786	valid_1's auc: 0.907133
[2900]	training's auc: 0.966999	valid_1's auc: 0.907199
[3000]	training's auc: 0.968169	valid_1's auc: 0.907296
Early stopping, best iteration is:
[2975]	training's auc: 0.967875	valid_1's auc: 0.907316
Partial score of fold 0 is: 0.9073160987038598
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.892346	valid_1's auc: 0.876736
[200]	training's auc: 0.900276	valid_1's auc: 0.881583
[300]	training's auc: 0.906675	valid_1's auc: 0.885374
[400]	training's auc: 0.912443	valid_1's auc: 0.888303
[500]	training's auc: 0.917509	valid_1's auc: 0.891351
[600]	training's auc: 0.922014	valid_1's auc: 0.89395
[700]	training's auc: 0.925866	valid_1's auc: 0.896018
[800]	training's auc: 0.929253	valid_1's auc: 0.897381
[900]	training's auc: 0.932244	valid_1's auc: 0.898599
[1000]	training's auc: 0.934919	valid_1's auc: 0.899559
[1100]	training's auc: 0.937427	valid_1's auc: 0.900361
[1200]	training's auc: 0.939818	valid_1's auc: 0.900963
[1300]	training's auc: 0.942058	valid_1's auc: 0.901445
[1400]	training's auc: 0.944186	valid_1's auc: 0.901931
[1500]	training's auc: 0.946247	valid_1's auc: 0.902428
[1600]	training's auc: 0.948228	valid_1's auc: 0.902727
[1700]	training's auc: 0.950023	valid_1's auc: 0.902984
[1800]	training's auc: 0.951801	valid_1's auc: 0.903294
[1900]	training's auc: 0.953541	valid_1's auc: 0.903601
[2000]	training's auc: 0.955211	valid_1's auc: 0.903864
[2100]	training's auc: 0.956767	valid_1's auc: 0.904029
[2200]	training's auc: 0.95831	valid_1's auc: 0.904132
[2300]	training's auc: 0.959775	valid_1's auc: 0.904268
[2400]	training's auc: 0.961201	valid_1's auc: 0.904356
[2500]	training's auc: 0.962562	valid_1's auc: 0.904465
[2600]	training's auc: 0.963873	valid_1's auc: 0.904531
[2700]	training's auc: 0.965105	valid_1's auc: 0.904552
[2800]	training's auc: 0.966324	valid_1's auc: 0.904598
[2900]	training's auc: 0.967479	valid_1's auc: 0.904599
[3000]	training's auc: 0.968538	valid_1's auc: 0.904656
[3100]	training's auc: 0.96962	valid_1's auc: 0.904663
[3200]	training's auc: 0.970673	valid_1's auc: 0.904746
[3300]	training's auc: 0.971642	valid_1's auc: 0.9048
[3400]	training's auc: 0.972587	valid_1's auc: 0.904823
Early stopping, best iteration is:
[3386]	training's auc: 0.97246	valid_1's auc: 0.904841
Partial score of fold 1 is: 0.9048408131142794
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.89287	valid_1's auc: 0.87563
[200]	training's auc: 0.901278	valid_1's auc: 0.8804
[300]	training's auc: 0.907518	valid_1's auc: 0.883654
[400]	training's auc: 0.913135	valid_1's auc: 0.886801
[500]	training's auc: 0.917731	valid_1's auc: 0.889265
[600]	training's auc: 0.922148	valid_1's auc: 0.891695
[700]	training's auc: 0.925907	valid_1's auc: 0.893697
[800]	training's auc: 0.92929	valid_1's auc: 0.895049
[900]	training's auc: 0.932287	valid_1's auc: 0.896157
[1000]	training's auc: 0.934942	valid_1's auc: 0.897066
[1100]	training's auc: 0.937475	valid_1's auc: 0.897738
[1200]	training's auc: 0.939901	valid_1's auc: 0.898262
[1300]	training's auc: 0.942137	valid_1's auc: 0.898625
[1400]	training's auc: 0.944218	valid_1's auc: 0.899047
[1500]	training's auc: 0.946227	valid_1's auc: 0.899396
[1600]	training's auc: 0.948203	valid_1's auc: 0.899639
[1700]	training's auc: 0.950093	valid_1's auc: 0.899902
[1800]	training's auc: 0.951893	valid_1's auc: 0.90013
[1900]	training's auc: 0.953656	valid_1's auc: 0.900375
[2000]	training's auc: 0.955287	valid_1's auc: 0.90048
[2100]	training's auc: 0.956887	valid_1's auc: 0.900598
[2200]	training's auc: 0.958384	valid_1's auc: 0.900765
[2300]	training's auc: 0.959846	valid_1's auc: 0.900923
[2400]	training's auc: 0.961195	valid_1's auc: 0.900974
[2500]	training's auc: 0.962572	valid_1's auc: 0.900999
Early stopping, best iteration is:
[2408]	training's auc: 0.961314	valid_1's auc: 0.901012
Partial score of fold 2 is: 0.9010123975352681
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.891784	valid_1's auc: 0.8824
[200]	training's auc: 0.899663	valid_1's auc: 0.887107
[300]	training's auc: 0.90657	valid_1's auc: 0.889657
[400]	training's auc: 0.912504	valid_1's auc: 0.892491
[500]	training's auc: 0.917349	valid_1's auc: 0.894724
[600]	training's auc: 0.921965	valid_1's auc: 0.89676
[700]	training's auc: 0.925854	valid_1's auc: 0.898291
[800]	training's auc: 0.929205	valid_1's auc: 0.899454
[900]	training's auc: 0.932205	valid_1's auc: 0.900315
[1000]	training's auc: 0.934961	valid_1's auc: 0.901067
[1100]	training's auc: 0.937431	valid_1's auc: 0.901548
[1200]	training's auc: 0.939843	valid_1's auc: 0.902116
[1300]	training's auc: 0.942111	valid_1's auc: 0.902504
[1400]	training's auc: 0.944267	valid_1's auc: 0.902885
[1500]	training's auc: 0.946349	valid_1's auc: 0.903228
[1600]	training's auc: 0.948361	valid_1's auc: 0.90339
[1700]	training's auc: 0.950238	valid_1's auc: 0.903584
[1800]	training's auc: 0.952046	valid_1's auc: 0.903855
[1900]	training's auc: 0.953848	valid_1's auc: 0.903963
[2000]	training's auc: 0.955521	valid_1's auc: 0.90411
[2100]	training's auc: 0.957149	valid_1's auc: 0.904196
[2200]	training's auc: 0.958648	valid_1's auc: 0.904327
[2300]	training's auc: 0.960085	valid_1's auc: 0.904394
[2400]	training's auc: 0.961519	valid_1's auc: 0.904397
Early stopping, best iteration is:
[2348]	training's auc: 0.960793	valid_1's auc: 0.904441
Partial score of fold 3 is: 0.9044406684378524
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.891653	valid_1's auc: 0.877568
[200]	training's auc: 0.899792	valid_1's auc: 0.882898
[300]	training's auc: 0.906243	valid_1's auc: 0.886354
[400]	training's auc: 0.911845	valid_1's auc: 0.889546
[500]	training's auc: 0.916721	valid_1's auc: 0.892351
[600]	training's auc: 0.921275	valid_1's auc: 0.895153
[700]	training's auc: 0.925218	valid_1's auc: 0.897425
[800]	training's auc: 0.928663	valid_1's auc: 0.899087
[900]	training's auc: 0.931684	valid_1's auc: 0.900365
[1000]	training's auc: 0.934446	valid_1's auc: 0.901322
[1100]	training's auc: 0.937012	valid_1's auc: 0.901999
[1200]	training's auc: 0.939412	valid_1's auc: 0.90261
[1300]	training's auc: 0.941649	valid_1's auc: 0.903145
[1400]	training's auc: 0.943834	valid_1's auc: 0.90359
[1500]	training's auc: 0.945858	valid_1's auc: 0.904039
[1600]	training's auc: 0.947864	valid_1's auc: 0.904413
[1700]	training's auc: 0.949763	valid_1's auc: 0.904584
[1800]	training's auc: 0.951581	valid_1's auc: 0.904969
[1900]	training's auc: 0.953296	valid_1's auc: 0.905244
[2000]	training's auc: 0.954949	valid_1's auc: 0.905463
[2100]	training's auc: 0.956523	valid_1's auc: 0.905644
[2200]	training's auc: 0.958053	valid_1's auc: 0.905869
[2300]	training's auc: 0.959543	valid_1's auc: 0.905985
[2400]	training's auc: 0.960927	valid_1's auc: 0.906076
[2500]	training's auc: 0.962311	valid_1's auc: 0.906183
[2600]	training's auc: 0.963639	valid_1's auc: 0.9062
[2700]	training's auc: 0.964885	valid_1's auc: 0.906326
[2800]	training's auc: 0.966127	valid_1's auc: 0.906435
[2900]	training's auc: 0.967198	valid_1's auc: 0.906501
[3000]	training's auc: 0.9683	valid_1's auc: 0.906566
[3100]	training's auc: 0.969397	valid_1's auc: 0.906628
[3200]	training's auc: 0.970447	valid_1's auc: 0.906696
[3300]	training's auc: 0.971429	valid_1's auc: 0.906687
Early stopping, best iteration is:
[3207]	training's auc: 0.970516	valid_1's auc: 0.906706
Partial score of fold 4 is: 0.9067055813383872
Our oof AUC score is:  0.9048373131808696
auc:  0.9048373131808696
|  30       |  0.9048   |  0.9417   |  4.621    |  4.761    |  0.005161 |  16.9     |  1.023    |  1.552    |
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.898655	valid_1's auc: 0.885387
[200]	training's auc: 0.907886	valid_1's auc: 0.890538
[300]	training's auc: 0.91534	valid_1's auc: 0.894171
[400]	training's auc: 0.921941	valid_1's auc: 0.897801
[500]	training's auc: 0.927383	valid_1's auc: 0.900532
[600]	training's auc: 0.931958	valid_1's auc: 0.902341
[700]	training's auc: 0.93613	valid_1's auc: 0.903663
[800]	training's auc: 0.939877	valid_1's auc: 0.904743
[900]	training's auc: 0.943338	valid_1's auc: 0.90546
[1000]	training's auc: 0.94652	valid_1's auc: 0.906109
[1100]	training's auc: 0.94962	valid_1's auc: 0.906575
[1200]	training's auc: 0.952558	valid_1's auc: 0.907122
[1300]	training's auc: 0.955272	valid_1's auc: 0.90749
[1400]	training's auc: 0.9578	valid_1's auc: 0.907919
[1500]	training's auc: 0.960266	valid_1's auc: 0.90819
[1600]	training's auc: 0.962519	valid_1's auc: 0.908425
[1700]	training's auc: 0.964646	valid_1's auc: 0.908643
[1800]	training's auc: 0.966701	valid_1's auc: 0.908754
[1900]	training's auc: 0.968612	valid_1's auc: 0.908875
[2000]	training's auc: 0.970393	valid_1's auc: 0.909064
[2100]	training's auc: 0.972102	valid_1's auc: 0.909184
[2200]	training's auc: 0.973676	valid_1's auc: 0.909242
[2300]	training's auc: 0.975214	valid_1's auc: 0.909308
[2400]	training's auc: 0.976716	valid_1's auc: 0.909426
[2500]	training's auc: 0.978089	valid_1's auc: 0.909487
[2600]	training's auc: 0.979339	valid_1's auc: 0.90959
[2700]	training's auc: 0.980507	valid_1's auc: 0.909615
[2800]	training's auc: 0.981676	valid_1's auc: 0.909647
[2900]	training's auc: 0.982798	valid_1's auc: 0.909679
[3000]	training's auc: 0.983825	valid_1's auc: 0.90969
Early stopping, best iteration is:
[2928]	training's auc: 0.983091	valid_1's auc: 0.909722
Partial score of fold 0 is: 0.9097218055201672
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.899331	valid_1's auc: 0.883205
[200]	training's auc: 0.90843	valid_1's auc: 0.888859
[300]	training's auc: 0.91596	valid_1's auc: 0.892746
[400]	training's auc: 0.922517	valid_1's auc: 0.896316
[500]	training's auc: 0.927834	valid_1's auc: 0.898833
[600]	training's auc: 0.932394	valid_1's auc: 0.900607
[700]	training's auc: 0.936397	valid_1's auc: 0.901798
[800]	training's auc: 0.940135	valid_1's auc: 0.902838
[900]	training's auc: 0.943728	valid_1's auc: 0.903528
[1000]	training's auc: 0.946921	valid_1's auc: 0.904301
[1100]	training's auc: 0.949958	valid_1's auc: 0.904862
[1200]	training's auc: 0.95289	valid_1's auc: 0.905434
[1300]	training's auc: 0.955638	valid_1's auc: 0.905721
[1400]	training's auc: 0.958253	valid_1's auc: 0.905976
[1500]	training's auc: 0.960728	valid_1's auc: 0.906187
[1600]	training's auc: 0.962999	valid_1's auc: 0.906363
[1700]	training's auc: 0.965139	valid_1's auc: 0.906452
[1800]	training's auc: 0.967109	valid_1's auc: 0.906583
[1900]	training's auc: 0.968996	valid_1's auc: 0.90675
[2000]	training's auc: 0.970757	valid_1's auc: 0.906912
[2100]	training's auc: 0.972435	valid_1's auc: 0.906947
[2200]	training's auc: 0.974066	valid_1's auc: 0.907018
Early stopping, best iteration is:
[2166]	training's auc: 0.973507	valid_1's auc: 0.907049
Partial score of fold 1 is: 0.9070493549452189
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.90022	valid_1's auc: 0.881589
[200]	training's auc: 0.908853	valid_1's auc: 0.886179
[300]	training's auc: 0.916354	valid_1's auc: 0.890488
[400]	training's auc: 0.922739	valid_1's auc: 0.894046
[500]	training's auc: 0.928092	valid_1's auc: 0.896515
[600]	training's auc: 0.932649	valid_1's auc: 0.898218
[700]	training's auc: 0.936714	valid_1's auc: 0.899347
[800]	training's auc: 0.940409	valid_1's auc: 0.900131
[900]	training's auc: 0.943962	valid_1's auc: 0.900767
[1000]	training's auc: 0.947161	valid_1's auc: 0.901296
[1100]	training's auc: 0.95021	valid_1's auc: 0.901727
[1200]	training's auc: 0.953202	valid_1's auc: 0.902184
[1300]	training's auc: 0.95599	valid_1's auc: 0.902452
[1400]	training's auc: 0.958474	valid_1's auc: 0.902826
[1500]	training's auc: 0.960895	valid_1's auc: 0.903055
[1600]	training's auc: 0.96317	valid_1's auc: 0.903243
[1700]	training's auc: 0.965322	valid_1's auc: 0.903413
[1800]	training's auc: 0.967345	valid_1's auc: 0.903565
[1900]	training's auc: 0.969249	valid_1's auc: 0.90371
[2000]	training's auc: 0.970992	valid_1's auc: 0.903796
[2100]	training's auc: 0.972643	valid_1's auc: 0.903795
Early stopping, best iteration is:
[2020]	training's auc: 0.971354	valid_1's auc: 0.903817
Partial score of fold 2 is: 0.9038170703944204
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.898934	valid_1's auc: 0.887186
[200]	training's auc: 0.907784	valid_1's auc: 0.891789
[300]	training's auc: 0.915539	valid_1's auc: 0.895476
[400]	training's auc: 0.922322	valid_1's auc: 0.898435
[500]	training's auc: 0.927716	valid_1's auc: 0.900297
[600]	training's auc: 0.932341	valid_1's auc: 0.901743
[700]	training's auc: 0.93647	valid_1's auc: 0.902816
[800]	training's auc: 0.940274	valid_1's auc: 0.90347
[900]	training's auc: 0.943865	valid_1's auc: 0.904059
[1000]	training's auc: 0.947175	valid_1's auc: 0.904654
[1100]	training's auc: 0.950388	valid_1's auc: 0.904928
[1200]	training's auc: 0.95338	valid_1's auc: 0.905198
[1300]	training's auc: 0.956143	valid_1's auc: 0.905486
[1400]	training's auc: 0.958752	valid_1's auc: 0.905644
[1500]	training's auc: 0.961172	valid_1's auc: 0.905876
[1600]	training's auc: 0.963402	valid_1's auc: 0.906048
[1700]	training's auc: 0.965506	valid_1's auc: 0.906272
[1800]	training's auc: 0.967485	valid_1's auc: 0.906432
[1900]	training's auc: 0.969481	valid_1's auc: 0.906488
[2000]	training's auc: 0.971253	valid_1's auc: 0.906597
[2100]	training's auc: 0.972965	valid_1's auc: 0.906646
[2200]	training's auc: 0.974538	valid_1's auc: 0.90668
[2300]	training's auc: 0.976014	valid_1's auc: 0.906693
[2400]	training's auc: 0.977385	valid_1's auc: 0.90673
[2500]	training's auc: 0.978802	valid_1's auc: 0.906778
[2600]	training's auc: 0.98002	valid_1's auc: 0.906832
[2700]	training's auc: 0.981179	valid_1's auc: 0.906862
Early stopping, best iteration is:
[2693]	training's auc: 0.981102	valid_1's auc: 0.906884
Partial score of fold 3 is: 0.9068835718973489
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.899114	valid_1's auc: 0.884592
[200]	training's auc: 0.907768	valid_1's auc: 0.889387
[300]	training's auc: 0.915178	valid_1's auc: 0.893431
[400]	training's auc: 0.921782	valid_1's auc: 0.897442
[500]	training's auc: 0.927283	valid_1's auc: 0.900448
[600]	training's auc: 0.931974	valid_1's auc: 0.902337
[700]	training's auc: 0.936159	valid_1's auc: 0.903491
[800]	training's auc: 0.939885	valid_1's auc: 0.904486
[900]	training's auc: 0.943541	valid_1's auc: 0.905253
[1000]	training's auc: 0.946854	valid_1's auc: 0.905901
[1100]	training's auc: 0.949891	valid_1's auc: 0.906524
[1200]	training's auc: 0.952882	valid_1's auc: 0.907077
[1300]	training's auc: 0.955659	valid_1's auc: 0.907399
[1400]	training's auc: 0.958193	valid_1's auc: 0.907727
[1500]	training's auc: 0.960663	valid_1's auc: 0.907959
[1600]	training's auc: 0.962934	valid_1's auc: 0.908127
[1700]	training's auc: 0.96505	valid_1's auc: 0.908364
[1800]	training's auc: 0.967053	valid_1's auc: 0.90857
[1900]	training's auc: 0.968974	valid_1's auc: 0.908777
[2000]	training's auc: 0.970691	valid_1's auc: 0.908905
[2100]	training's auc: 0.972472	valid_1's auc: 0.908912
[2200]	training's auc: 0.974028	valid_1's auc: 0.90893
[2300]	training's auc: 0.975516	valid_1's auc: 0.908982
[2400]	training's auc: 0.976965	valid_1's auc: 0.908977
Early stopping, best iteration is:
[2333]	training's auc: 0.976012	valid_1's auc: 0.909023
Partial score of fold 4 is: 0.9090229264429703
Our oof AUC score is:  0.9072497987482065
auc:  0.9072497987482065
|  31       |  0.9072   |  0.636    |  0.04764  |  4.776    |  0.007769 |  16.56    |  1.119    |  1.532    |
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.900973	valid_1's auc: 0.884952
[200]	training's auc: 0.912573	valid_1's auc: 0.890233
[300]	training's auc: 0.921171	valid_1's auc: 0.894842
[400]	training's auc: 0.928109	valid_1's auc: 0.898143
[500]	training's auc: 0.933715	valid_1's auc: 0.900248
[600]	training's auc: 0.938491	valid_1's auc: 0.901761
[700]	training's auc: 0.94278	valid_1's auc: 0.902929
[800]	training's auc: 0.946664	valid_1's auc: 0.903839
[900]	training's auc: 0.950258	valid_1's auc: 0.904602
[1000]	training's auc: 0.9536	valid_1's auc: 0.905219
[1100]	training's auc: 0.956829	valid_1's auc: 0.905649
[1200]	training's auc: 0.959828	valid_1's auc: 0.906074
[1300]	training's auc: 0.962569	valid_1's auc: 0.906437
[1400]	training's auc: 0.965005	valid_1's auc: 0.906588
[1500]	training's auc: 0.967356	valid_1's auc: 0.906752
[1600]	training's auc: 0.969535	valid_1's auc: 0.906936
[1700]	training's auc: 0.971541	valid_1's auc: 0.907045
[1800]	training's auc: 0.973425	valid_1's auc: 0.90712
[1900]	training's auc: 0.975185	valid_1's auc: 0.907303
[2000]	training's auc: 0.976793	valid_1's auc: 0.907368
[2100]	training's auc: 0.978272	valid_1's auc: 0.907424
[2200]	training's auc: 0.979728	valid_1's auc: 0.907493
[2300]	training's auc: 0.981056	valid_1's auc: 0.90765
[2400]	training's auc: 0.982365	valid_1's auc: 0.907661
Early stopping, best iteration is:
[2371]	training's auc: 0.982008	valid_1's auc: 0.907688
Partial score of fold 0 is: 0.9076875498471981
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.901375	valid_1's auc: 0.881745
[200]	training's auc: 0.912832	valid_1's auc: 0.88787
[300]	training's auc: 0.921383	valid_1's auc: 0.892287
[400]	training's auc: 0.928448	valid_1's auc: 0.895839
[500]	training's auc: 0.934094	valid_1's auc: 0.898101
[600]	training's auc: 0.938883	valid_1's auc: 0.899576
[700]	training's auc: 0.943077	valid_1's auc: 0.900762
[800]	training's auc: 0.946875	valid_1's auc: 0.901635
[900]	training's auc: 0.950552	valid_1's auc: 0.902358
[1000]	training's auc: 0.953838	valid_1's auc: 0.902926
[1100]	training's auc: 0.956973	valid_1's auc: 0.903317
[1200]	training's auc: 0.959969	valid_1's auc: 0.903701
[1300]	training's auc: 0.962731	valid_1's auc: 0.903856
[1400]	training's auc: 0.965369	valid_1's auc: 0.904167
[1500]	training's auc: 0.967768	valid_1's auc: 0.904344
[1600]	training's auc: 0.969873	valid_1's auc: 0.904428
[1700]	training's auc: 0.971883	valid_1's auc: 0.904572
[1800]	training's auc: 0.973755	valid_1's auc: 0.904797
[1900]	training's auc: 0.975516	valid_1's auc: 0.904936
[2000]	training's auc: 0.977164	valid_1's auc: 0.905102
[2100]	training's auc: 0.97869	valid_1's auc: 0.905131
Early stopping, best iteration is:
[2012]	training's auc: 0.977356	valid_1's auc: 0.905141
Partial score of fold 1 is: 0.9051408385938304
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.902038	valid_1's auc: 0.880248
[200]	training's auc: 0.913162	valid_1's auc: 0.885394
[300]	training's auc: 0.921695	valid_1's auc: 0.890112
[400]	training's auc: 0.928666	valid_1's auc: 0.893527
[500]	training's auc: 0.934286	valid_1's auc: 0.895723
[600]	training's auc: 0.939117	valid_1's auc: 0.897066
[700]	training's auc: 0.943321	valid_1's auc: 0.897929
[800]	training's auc: 0.94719	valid_1's auc: 0.89869
[900]	training's auc: 0.950756	valid_1's auc: 0.899334
[1000]	training's auc: 0.954059	valid_1's auc: 0.899639
[1100]	training's auc: 0.957321	valid_1's auc: 0.900178
[1200]	training's auc: 0.960237	valid_1's auc: 0.900616
[1300]	training's auc: 0.962941	valid_1's auc: 0.900887
[1400]	training's auc: 0.965365	valid_1's auc: 0.901135
[1500]	training's auc: 0.967666	valid_1's auc: 0.901366
[1600]	training's auc: 0.969889	valid_1's auc: 0.901528
[1700]	training's auc: 0.971895	valid_1's auc: 0.901683
[1800]	training's auc: 0.97377	valid_1's auc: 0.901874
[1900]	training's auc: 0.975576	valid_1's auc: 0.901867
Early stopping, best iteration is:
[1833]	training's auc: 0.97439	valid_1's auc: 0.901909
Partial score of fold 2 is: 0.9019085917346749
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.901322	valid_1's auc: 0.88713
[200]	training's auc: 0.912892	valid_1's auc: 0.891667
[300]	training's auc: 0.921663	valid_1's auc: 0.89551
[400]	training's auc: 0.928625	valid_1's auc: 0.898135
[500]	training's auc: 0.934207	valid_1's auc: 0.899831
[600]	training's auc: 0.938917	valid_1's auc: 0.901065
[700]	training's auc: 0.943256	valid_1's auc: 0.901966
[800]	training's auc: 0.947072	valid_1's auc: 0.902582
[900]	training's auc: 0.950695	valid_1's auc: 0.903042
[1000]	training's auc: 0.954213	valid_1's auc: 0.903497
[1100]	training's auc: 0.957428	valid_1's auc: 0.903788
[1200]	training's auc: 0.960413	valid_1's auc: 0.904064
[1300]	training's auc: 0.963176	valid_1's auc: 0.9042
[1400]	training's auc: 0.965668	valid_1's auc: 0.904476
[1500]	training's auc: 0.967977	valid_1's auc: 0.90459
[1600]	training's auc: 0.970152	valid_1's auc: 0.904656
[1700]	training's auc: 0.972142	valid_1's auc: 0.904807
[1800]	training's auc: 0.973993	valid_1's auc: 0.904885
[1900]	training's auc: 0.975774	valid_1's auc: 0.904958
[2000]	training's auc: 0.977358	valid_1's auc: 0.904924
Early stopping, best iteration is:
[1907]	training's auc: 0.975885	valid_1's auc: 0.904976
Partial score of fold 3 is: 0.9049756578498265
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.901845	valid_1's auc: 0.882964
[200]	training's auc: 0.912768	valid_1's auc: 0.888405
[300]	training's auc: 0.921322	valid_1's auc: 0.893408
[400]	training's auc: 0.928296	valid_1's auc: 0.897497
[500]	training's auc: 0.933917	valid_1's auc: 0.90005
[600]	training's auc: 0.93877	valid_1's auc: 0.901715
[700]	training's auc: 0.943146	valid_1's auc: 0.902741
[800]	training's auc: 0.947	valid_1's auc: 0.903739
[900]	training's auc: 0.950613	valid_1's auc: 0.904419
[1000]	training's auc: 0.953997	valid_1's auc: 0.904888
[1100]	training's auc: 0.9571	valid_1's auc: 0.905185
[1200]	training's auc: 0.960057	valid_1's auc: 0.905574
[1300]	training's auc: 0.962835	valid_1's auc: 0.905856
[1400]	training's auc: 0.965337	valid_1's auc: 0.906091
[1500]	training's auc: 0.96768	valid_1's auc: 0.906274
[1600]	training's auc: 0.969904	valid_1's auc: 0.906482
[1700]	training's auc: 0.971931	valid_1's auc: 0.906611
[1800]	training's auc: 0.973804	valid_1's auc: 0.906896
[1900]	training's auc: 0.975505	valid_1's auc: 0.906973
[2000]	training's auc: 0.977032	valid_1's auc: 0.90712
[2100]	training's auc: 0.978606	valid_1's auc: 0.90722
Early stopping, best iteration is:
[2067]	training's auc: 0.978098	valid_1's auc: 0.907295
Partial score of fold 4 is: 0.9072954530574024
Our oof AUC score is:  0.9053810190637271
auc:  0.9053810190637271
|  32       |  0.9054   |  0.9233   |  0.7025   |  4.987    |  0.008032 |  16.94    |  1.605    |  1.04     |
=============================================================================================================

LGB_BO_v2.max['params']

{'feature_fraction': 0.524207414205945,
 'lambda_l1': 4.171808735757517,
 'lambda_l2': 4.6435328298317256,
 'learning_rate': 0.007897539397989824,
 'max_depth': 16.62053004755999,
 'scale_pos_weight': 1.2199266532301127,
 'subsample_freq': 1.0276518730971627}

if boll_BayesianOptimization: # ACTIVATE it if you want to search/use for better parameter
    lgb_model_v2 = Lgb_Model(train,test, features, categoricals=categoricals_features, ps= LGB_BO_v2.max['params'])
else :
    lgb_model_v2 = Lgb_Model(train,test, features, categoricals=categoricals_features, ps=params)

Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.899358	valid_1's auc: 0.886976
[200]	training's auc: 0.907669	valid_1's auc: 0.891791
[300]	training's auc: 0.914542	valid_1's auc: 0.895347
[400]	training's auc: 0.920361	valid_1's auc: 0.898506
[500]	training's auc: 0.92523	valid_1's auc: 0.900896
[600]	training's auc: 0.929528	valid_1's auc: 0.902815
[700]	training's auc: 0.933281	valid_1's auc: 0.904161
[800]	training's auc: 0.936711	valid_1's auc: 0.905167
[900]	training's auc: 0.939914	valid_1's auc: 0.905877
[1000]	training's auc: 0.942914	valid_1's auc: 0.906541
[1100]	training's auc: 0.945717	valid_1's auc: 0.906978
[1200]	training's auc: 0.948436	valid_1's auc: 0.907454
[1300]	training's auc: 0.950997	valid_1's auc: 0.907894
[1400]	training's auc: 0.953357	valid_1's auc: 0.90822
[1500]	training's auc: 0.955656	valid_1's auc: 0.90851
[1600]	training's auc: 0.957823	valid_1's auc: 0.90876
[1700]	training's auc: 0.959913	valid_1's auc: 0.908919
[1800]	training's auc: 0.961929	valid_1's auc: 0.9091
[1900]	training's auc: 0.963788	valid_1's auc: 0.909298
[2000]	training's auc: 0.965544	valid_1's auc: 0.909451
[2100]	training's auc: 0.967185	valid_1's auc: 0.909556
[2200]	training's auc: 0.968755	valid_1's auc: 0.909665
[2300]	training's auc: 0.970308	valid_1's auc: 0.909798
[2400]	training's auc: 0.971786	valid_1's auc: 0.909794
[2500]	training's auc: 0.973125	valid_1's auc: 0.909857
[2600]	training's auc: 0.97446	valid_1's auc: 0.909923
[2700]	training's auc: 0.975661	valid_1's auc: 0.909942
Early stopping, best iteration is:
[2643]	training's auc: 0.974999	valid_1's auc: 0.909959
Partial score of fold 0 is: 0.9099590744138573
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.899598	valid_1's auc: 0.885021
[200]	training's auc: 0.907759	valid_1's auc: 0.889878
[300]	training's auc: 0.91485	valid_1's auc: 0.893416
[400]	training's auc: 0.920711	valid_1's auc: 0.896422
[500]	training's auc: 0.925693	valid_1's auc: 0.898995
[600]	training's auc: 0.929923	valid_1's auc: 0.900811
[700]	training's auc: 0.933601	valid_1's auc: 0.902158
[800]	training's auc: 0.936951	valid_1's auc: 0.903225
[900]	training's auc: 0.940162	valid_1's auc: 0.904073
[1000]	training's auc: 0.943143	valid_1's auc: 0.90483
[1100]	training's auc: 0.945996	valid_1's auc: 0.905314
[1200]	training's auc: 0.948736	valid_1's auc: 0.905832
[1300]	training's auc: 0.951279	valid_1's auc: 0.906225
[1400]	training's auc: 0.953673	valid_1's auc: 0.906559
[1500]	training's auc: 0.955977	valid_1's auc: 0.906753
[1600]	training's auc: 0.958108	valid_1's auc: 0.906796
[1700]	training's auc: 0.960178	valid_1's auc: 0.906925
[1800]	training's auc: 0.962136	valid_1's auc: 0.907044
[1900]	training's auc: 0.964001	valid_1's auc: 0.907168
[2000]	training's auc: 0.965698	valid_1's auc: 0.907241
[2100]	training's auc: 0.967362	valid_1's auc: 0.907282
Early stopping, best iteration is:
[2032]	training's auc: 0.966251	valid_1's auc: 0.907315
Partial score of fold 1 is: 0.9073147418047061
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.900782	valid_1's auc: 0.882618
[200]	training's auc: 0.908633	valid_1's auc: 0.887012
[300]	training's auc: 0.915261	valid_1's auc: 0.890628
[400]	training's auc: 0.92104	valid_1's auc: 0.893789
[500]	training's auc: 0.925891	valid_1's auc: 0.896184
[600]	training's auc: 0.930131	valid_1's auc: 0.897768
[700]	training's auc: 0.933908	valid_1's auc: 0.898875
[800]	training's auc: 0.937272	valid_1's auc: 0.899895
[900]	training's auc: 0.94044	valid_1's auc: 0.900628
[1000]	training's auc: 0.943377	valid_1's auc: 0.901196
[1100]	training's auc: 0.946279	valid_1's auc: 0.901509
[1200]	training's auc: 0.948943	valid_1's auc: 0.901901
[1300]	training's auc: 0.951434	valid_1's auc: 0.902266
[1400]	training's auc: 0.953821	valid_1's auc: 0.902558
[1500]	training's auc: 0.95614	valid_1's auc: 0.902864
[1600]	training's auc: 0.958298	valid_1's auc: 0.903075
[1700]	training's auc: 0.960317	valid_1's auc: 0.903315
[1800]	training's auc: 0.962192	valid_1's auc: 0.903397
[1900]	training's auc: 0.964027	valid_1's auc: 0.903598
[2000]	training's auc: 0.965784	valid_1's auc: 0.903705
[2100]	training's auc: 0.967416	valid_1's auc: 0.903731
[2200]	training's auc: 0.969026	valid_1's auc: 0.903885
[2300]	training's auc: 0.970572	valid_1's auc: 0.903897
[2400]	training's auc: 0.971953	valid_1's auc: 0.904012
[2500]	training's auc: 0.973276	valid_1's auc: 0.904062
[2600]	training's auc: 0.974591	valid_1's auc: 0.90408
[2700]	training's auc: 0.975829	valid_1's auc: 0.904138
[2800]	training's auc: 0.977045	valid_1's auc: 0.904177
[2900]	training's auc: 0.978182	valid_1's auc: 0.904241
[3000]	training's auc: 0.979279	valid_1's auc: 0.904186
Early stopping, best iteration is:
[2915]	training's auc: 0.978346	valid_1's auc: 0.904257
Partial score of fold 2 is: 0.9042566303369483
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.899869	valid_1's auc: 0.888264
[200]	training's auc: 0.907982	valid_1's auc: 0.892879
[300]	training's auc: 0.914798	valid_1's auc: 0.896127
[400]	training's auc: 0.920801	valid_1's auc: 0.898862
[500]	training's auc: 0.925628	valid_1's auc: 0.90076
[600]	training's auc: 0.929934	valid_1's auc: 0.902154
[700]	training's auc: 0.933667	valid_1's auc: 0.903222
[800]	training's auc: 0.937023	valid_1's auc: 0.904127
[900]	training's auc: 0.940284	valid_1's auc: 0.904678
[1000]	training's auc: 0.943252	valid_1's auc: 0.90518
[1100]	training's auc: 0.946159	valid_1's auc: 0.905606
[1200]	training's auc: 0.948909	valid_1's auc: 0.905944
[1300]	training's auc: 0.951474	valid_1's auc: 0.906274
[1400]	training's auc: 0.953954	valid_1's auc: 0.90652
[1500]	training's auc: 0.956242	valid_1's auc: 0.906657
[1600]	training's auc: 0.958421	valid_1's auc: 0.906822
[1700]	training's auc: 0.960432	valid_1's auc: 0.907026
[1800]	training's auc: 0.962328	valid_1's auc: 0.907088
[1900]	training's auc: 0.964192	valid_1's auc: 0.907162
[2000]	training's auc: 0.965914	valid_1's auc: 0.907201
Early stopping, best iteration is:
[1967]	training's auc: 0.965354	valid_1's auc: 0.907245
Partial score of fold 3 is: 0.907244867854072
Training until validation scores don't improve for 100 rounds
[100]	training's auc: 0.899802	valid_1's auc: 0.885389
[200]	training's auc: 0.907617	valid_1's auc: 0.890191
[300]	training's auc: 0.91444	valid_1's auc: 0.893913
[400]	training's auc: 0.920195	valid_1's auc: 0.897459
[500]	training's auc: 0.925221	valid_1's auc: 0.900295
[600]	training's auc: 0.929517	valid_1's auc: 0.902334
[700]	training's auc: 0.933258	valid_1's auc: 0.903623
[800]	training's auc: 0.936676	valid_1's auc: 0.904537
[900]	training's auc: 0.939914	valid_1's auc: 0.905354
[1000]	training's auc: 0.942962	valid_1's auc: 0.906036
[1100]	training's auc: 0.945841	valid_1's auc: 0.906568
[1200]	training's auc: 0.948608	valid_1's auc: 0.907135
[1300]	training's auc: 0.951153	valid_1's auc: 0.907547
[1400]	training's auc: 0.953636	valid_1's auc: 0.907967
[1500]	training's auc: 0.955902	valid_1's auc: 0.908143
[1600]	training's auc: 0.958035	valid_1's auc: 0.908402
[1700]	training's auc: 0.960098	valid_1's auc: 0.908647
[1800]	training's auc: 0.962062	valid_1's auc: 0.908781
[1900]	training's auc: 0.963887	valid_1's auc: 0.908894
[2000]	training's auc: 0.965633	valid_1's auc: 0.908983
[2100]	training's auc: 0.967299	valid_1's auc: 0.90909
[2200]	training's auc: 0.968846	valid_1's auc: 0.909291
[2300]	training's auc: 0.970359	valid_1's auc: 0.909271
Early stopping, best iteration is:
[2237]	training's auc: 0.969396	valid_1's auc: 0.909322
Partial score of fold 4 is: 0.9093215751508595
Our oof AUC score is:  0.9075241899054636

# Plot Feat Importance

imp_df_v2 = pd.DataFrame()
imp_df_v2['feature'] = features
imp_df_v2['gain']  = lgb_model_v2.model.feature_importance(importance_type='gain')
imp_df_v2['split'] = lgb_model_v2.model.feature_importance(importance_type='split')

plot_importances(imp_df_v2)

import warnings
warnings.filterwarnings("ignore")
warnings.simplefilter(action='ignore', category=UserWarning)
i=0
for index, row in imp_df_v2.sort_values(by=['gain'],ascending=False).iterrows():  
    column=row['feature']
    if i< 50:
            print(column,i,"gain :",row['gain'])
            df1      = train.loc[train['hospital_death']==0]
            df2      = train.loc[train['hospital_death']==1]

            fig = plt.figure(figsize=(20,4))
            sns.distplot(df1[column].dropna(),  color='red', label='hospital_death 0', kde=True); 
            sns.distplot(df2[column].dropna(),  color='blue', label='hospital_death 1', kde=True); 
            fig=plt.legend(loc='best')
            plt.xlabel(column, fontsize=12);
            plt.show()
            i=i+1

apache_4a_hospital_death_prob 0 gain : 329680.2181470394

apache_4a_icu_death_prob 1 gain : 156631.5116765499

hospital_id 2 gain : 145410.42276763916

d1_lactate_min 3 gain : 60796.08799648285

d1_spo2_min 4 gain : 32836.89942884445

ventilated_apache 5 gain : 30552.519705295563

age 6 gain : 22173.600246667862

d1_sysbp_min 7 gain : 22129.421936273575

d1_heartrate_min 8 gain : 21593.396679878235

d1_bun_min 9 gain : 17723.877878189087

apache_3j_diagnosis 10 gain : 17544.95256614685

d1_sysbp_noninvasive_min 11 gain : 16455.46886730194

d1_lactate_max 12 gain : 16348.615434885025

d1_temp_max 13 gain : 15951.985478878021

gcs_motor_apache 14 gain : 14751.15071105957

urineoutput_apache 15 gain : 13288.36552143097

d1_bun_max 16 gain : 13152.739966869354

d1_platelets_min 17 gain : 12602.045546770096

gcs_eyes_apache 18 gain : 12534.613032341003

d1_resprate_min 19 gain : 11451.371369123459

d1_arterial_ph_min 20 gain : 11213.823065042496

d1_temp_min 21 gain : 11016.257133483887

bmi 22 gain : 10569.011886119843

d1_resprate_max 23 gain : 10451.489589452744

apache_3j_bodysystem 24 gain : 10363.706548213959

d1_heartrate_max 25 gain : 9830.411767721176

d1_glucose_min 26 gain : 9034.130075931549

wbc_apache 27 gain : 8726.267885684967

d1_wbc_min 28 gain : 8554.629989147186

creatinine_apache 29 gain : 8105.224138975143

h1_resprate_min 30 gain : 7891.371155738831

apache_2_diagnosis 31 gain : 7832.032730102539

d1_sodium_max 32 gain : 7768.3484926223755

d1_pao2fio2ratio_max 33 gain : 7706.5754590034485

pre_icu_los_days 34 gain : 7615.919073820114

d1_arterial_ph_max 35 gain : 7319.393048524857

glucose_apache 36 gain : 7211.341676950455

weight 37 gain : 7177.361359834671

temp_apache 38 gain : 7013.908061504364

d1_platelets_max 39 gain : 6979.343486785889

apache_2_bodysystem 40 gain : 6878.724554777145

d1_arterial_po2_max 41 gain : 6779.606928348541

d1_pao2fio2ratio_min 42 gain : 6671.06258058548

d1_hco3_min 43 gain : 6501.838881731033

d1_sysbp_noninvasive_max 44 gain : 6441.646271944046

d1_mbp_noninvasive_min 45 gain : 6383.477047920227

d1_hco3_max 46 gain : 6359.334758520126

d1_inr_max 47 gain : 5976.186361789703

bun_apache 48 gain : 5789.338826417923

d1_arterial_po2_min 49 gain : 5672.42506146431

Submissing File

test["hospital_death"] = lgb_model_v2.y_pred
test[["encounter_id","hospital_death"]].to_csv("submission4-lgb-v3.csv",index=False)

test[["encounter_id","hospital_death"]].head()

	encounter_id	hospital_death
0	2	0.01
1	5	0.02
2	7	0.01
3	8	0.12
4	10	0.66